In [19]:
plt.imshow(X_train[random_num])
WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Out[19]:
<matplotlib.image.AxesImage at 0x7ff8ec2caa00>
from google.colab import drive
drive.mount('/content/drive')
folderPath = "/content/drive/MyDrive/Colab4GPU/Project2"
# folderPath = "."
Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount("/content/drive", force_remount=True).
import random
random.seed(0)
# Ignore the warnings
import warnings
warnings.filterwarnings("ignore")
import os
import numpy as np
import pandas as pd
import cv2
from glob import glob
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from tensorflow.keras.layers import Concatenate, UpSampling2D, Conv2D, Reshape, Input, Dense, Dropout, Softmax, Flatten, Activation, BatchNormalization
from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau, ModelCheckpoint
from tensorflow.keras.metrics import Recall, Precision
from tensorflow.keras.applications.mobilenet import preprocess_input
import tensorflow as tf
from PIL import Image
from numpy import asarray
from tensorflow.keras.applications.mobilenet import MobileNet
from tensorflow.keras.applications import MobileNetV2
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.losses import binary_crossentropy
from tensorflow.keras.backend import log, epsilon
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import ZeroPadding2D, Convolution2D, MaxPooling2D
IMAGE_WIDTH = 224
IMAGE_HEIGHT = 224
IMAGE_SIZE = 224
HEIGHT_CELLS = 28
WIDTH_CELLS = 28
BATCH_SIZE = 3
ALPHA = 1
1.A.
data = np.load(folderPath + '/images.npy', allow_pickle=True)
data.shape
(409, 2)
1.B.
data[0]
array([array([[[42, 37, 34],
[56, 51, 48],
[71, 66, 63],
...,
[23, 33, 34],
[26, 36, 37],
[28, 38, 39]],
[[40, 35, 32],
[51, 46, 43],
[64, 59, 56],
...,
[27, 36, 35],
[24, 33, 32],
[26, 35, 34]],
[[43, 38, 35],
[51, 46, 43],
[61, 56, 53],
...,
[28, 30, 27],
[33, 35, 32],
[35, 37, 34]],
...,
[[56, 47, 40],
[57, 48, 41],
[61, 52, 45],
...,
[67, 48, 42],
[55, 35, 28],
[60, 40, 33]],
[[53, 44, 37],
[54, 45, 38],
[57, 48, 41],
...,
[59, 40, 34],
[60, 40, 33],
[54, 34, 27]],
[[53, 44, 37],
[54, 45, 38],
[57, 48, 41],
...,
[59, 40, 34],
[70, 50, 43],
[64, 44, 37]]], dtype=uint8),
list([{'label': ['Face'], 'notes': '', 'points': [{'x': 0.08615384615384615, 'y': 0.3063063063063063}, {'x': 0.1723076923076923, 'y': 0.45345345345345345}], 'imageWidth': 650, 'imageHeight': 333}, {'label': ['Face'], 'notes': '', 'points': [{'x': 0.583076923076923, 'y': 0.2912912912912913}, {'x': 0.6584615384615384, 'y': 0.46846846846846846}], 'imageWidth': 650, 'imageHeight': 333}])],
dtype=object)
data[0][0]
array([[[42, 37, 34],
[56, 51, 48],
[71, 66, 63],
...,
[23, 33, 34],
[26, 36, 37],
[28, 38, 39]],
[[40, 35, 32],
[51, 46, 43],
[64, 59, 56],
...,
[27, 36, 35],
[24, 33, 32],
[26, 35, 34]],
[[43, 38, 35],
[51, 46, 43],
[61, 56, 53],
...,
[28, 30, 27],
[33, 35, 32],
[35, 37, 34]],
...,
[[56, 47, 40],
[57, 48, 41],
[61, 52, 45],
...,
[67, 48, 42],
[55, 35, 28],
[60, 40, 33]],
[[53, 44, 37],
[54, 45, 38],
[57, 48, 41],
...,
[59, 40, 34],
[60, 40, 33],
[54, 34, 27]],
[[53, 44, 37],
[54, 45, 38],
[57, 48, 41],
...,
[59, 40, 34],
[70, 50, 43],
[64, 44, 37]]], dtype=uint8)
data[0][1]
[{'label': ['Face'],
'notes': '',
'points': [{'x': 0.08615384615384615, 'y': 0.3063063063063063},
{'x': 0.1723076923076923, 'y': 0.45345345345345345}],
'imageWidth': 650,
'imageHeight': 333},
{'label': ['Face'],
'notes': '',
'points': [{'x': 0.583076923076923, 'y': 0.2912912912912913},
{'x': 0.6584615384615384, 'y': 0.46846846846846846}],
'imageWidth': 650,
'imageHeight': 333}]
Spliting data into Features(X) & labels(Y)
X = data[:,0]
X.shape
(409,)
Y = data[:,1]
Y.shape
(409,)
images = np.zeros((int(data.shape[0]), IMAGE_HEIGHT, IMAGE_WIDTH, 3))
masks = np.zeros((int(data.shape[0]), IMAGE_HEIGHT, IMAGE_WIDTH))
for index in range(data.shape[0]):
img = data[index][0]
img = cv2.resize(img, dsize=(IMAGE_HEIGHT, IMAGE_WIDTH), interpolation=cv2.INTER_CUBIC)
try:
img = img[:,:,:3]
except:
print(f"Exception {index} Grayscale image with shape {img.shape}")
img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
continue
images[index] = preprocess_input(np.array(img, dtype=np.float32))
for i in data[index][1]:
x1 = int(i['points'][0]['x'] * IMAGE_WIDTH)
x2 = int(i['points'][1]['x'] * IMAGE_WIDTH)
y1 = int(i['points'][0]['y'] * IMAGE_HEIGHT)
y2 = int(i['points'][1]['y'] * IMAGE_HEIGHT)
# setting mask co-ordinates to 1.
masks[index][y1:y2, x1:x2] = 1
print(f"### Shape of X is '{images.shape}' and the shape of mask is '{masks.shape}' ")
Exception 272 Grayscale image with shape (224, 224) ### Shape of X is '(409, 224, 224, 3)' and the shape of mask is '(409, 224, 224)'
1.C.
X_train, X_test, y_train, y_test = train_test_split(images, masks, test_size=9, train_size=400)
X_test, X_val, y_test, y_val = train_test_split(X_test, y_test, test_size=0.2)
X_train.shape, X_test.shape, y_train.shape, y_test.shape, X_val.shape, y_val.shape
((400, 224, 224, 3), (7, 224, 224, 3), (400, 224, 224), (7, 224, 224), (2, 224, 224, 3), (2, 224, 224))
y_train[0]
array([[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
...,
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.]])
1.D.
random_num = np.random.choice(len(X_train))
random_num
179
Displaying original image
plt.imshow(X_train[random_num])
WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
<matplotlib.image.AxesImage at 0x7ff8ec2caa00>
Masked image
plt.imshow(y_train[random_num])
<matplotlib.image.AxesImage at 0x7ff8ea5a0ac0>
2.A.
model = MobileNet(input_shape=(IMAGE_HEIGHT, IMAGE_WIDTH, 3), include_top=False, alpha=ALPHA, weights="imagenet")
model.summary()
Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet/mobilenet_1_0_224_tf_no_top.h5
17225924/17225924 [==============================] - 0s 0us/step
Model: "mobilenet_1.00_224"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) [(None, 224, 224, 3)] 0
conv1 (Conv2D) (None, 112, 112, 32) 864
conv1_bn (BatchNormalizatio (None, 112, 112, 32) 128
n)
conv1_relu (ReLU) (None, 112, 112, 32) 0
conv_dw_1 (DepthwiseConv2D) (None, 112, 112, 32) 288
conv_dw_1_bn (BatchNormaliz (None, 112, 112, 32) 128
ation)
conv_dw_1_relu (ReLU) (None, 112, 112, 32) 0
conv_pw_1 (Conv2D) (None, 112, 112, 64) 2048
conv_pw_1_bn (BatchNormaliz (None, 112, 112, 64) 256
ation)
conv_pw_1_relu (ReLU) (None, 112, 112, 64) 0
conv_pad_2 (ZeroPadding2D) (None, 113, 113, 64) 0
conv_dw_2 (DepthwiseConv2D) (None, 56, 56, 64) 576
conv_dw_2_bn (BatchNormaliz (None, 56, 56, 64) 256
ation)
conv_dw_2_relu (ReLU) (None, 56, 56, 64) 0
conv_pw_2 (Conv2D) (None, 56, 56, 128) 8192
conv_pw_2_bn (BatchNormaliz (None, 56, 56, 128) 512
ation)
conv_pw_2_relu (ReLU) (None, 56, 56, 128) 0
conv_dw_3 (DepthwiseConv2D) (None, 56, 56, 128) 1152
conv_dw_3_bn (BatchNormaliz (None, 56, 56, 128) 512
ation)
conv_dw_3_relu (ReLU) (None, 56, 56, 128) 0
conv_pw_3 (Conv2D) (None, 56, 56, 128) 16384
conv_pw_3_bn (BatchNormaliz (None, 56, 56, 128) 512
ation)
conv_pw_3_relu (ReLU) (None, 56, 56, 128) 0
conv_pad_4 (ZeroPadding2D) (None, 57, 57, 128) 0
conv_dw_4 (DepthwiseConv2D) (None, 28, 28, 128) 1152
conv_dw_4_bn (BatchNormaliz (None, 28, 28, 128) 512
ation)
conv_dw_4_relu (ReLU) (None, 28, 28, 128) 0
conv_pw_4 (Conv2D) (None, 28, 28, 256) 32768
conv_pw_4_bn (BatchNormaliz (None, 28, 28, 256) 1024
ation)
conv_pw_4_relu (ReLU) (None, 28, 28, 256) 0
conv_dw_5 (DepthwiseConv2D) (None, 28, 28, 256) 2304
conv_dw_5_bn (BatchNormaliz (None, 28, 28, 256) 1024
ation)
conv_dw_5_relu (ReLU) (None, 28, 28, 256) 0
conv_pw_5 (Conv2D) (None, 28, 28, 256) 65536
conv_pw_5_bn (BatchNormaliz (None, 28, 28, 256) 1024
ation)
conv_pw_5_relu (ReLU) (None, 28, 28, 256) 0
conv_pad_6 (ZeroPadding2D) (None, 29, 29, 256) 0
conv_dw_6 (DepthwiseConv2D) (None, 14, 14, 256) 2304
conv_dw_6_bn (BatchNormaliz (None, 14, 14, 256) 1024
ation)
conv_dw_6_relu (ReLU) (None, 14, 14, 256) 0
conv_pw_6 (Conv2D) (None, 14, 14, 512) 131072
conv_pw_6_bn (BatchNormaliz (None, 14, 14, 512) 2048
ation)
conv_pw_6_relu (ReLU) (None, 14, 14, 512) 0
conv_dw_7 (DepthwiseConv2D) (None, 14, 14, 512) 4608
conv_dw_7_bn (BatchNormaliz (None, 14, 14, 512) 2048
ation)
conv_dw_7_relu (ReLU) (None, 14, 14, 512) 0
conv_pw_7 (Conv2D) (None, 14, 14, 512) 262144
conv_pw_7_bn (BatchNormaliz (None, 14, 14, 512) 2048
ation)
conv_pw_7_relu (ReLU) (None, 14, 14, 512) 0
conv_dw_8 (DepthwiseConv2D) (None, 14, 14, 512) 4608
conv_dw_8_bn (BatchNormaliz (None, 14, 14, 512) 2048
ation)
conv_dw_8_relu (ReLU) (None, 14, 14, 512) 0
conv_pw_8 (Conv2D) (None, 14, 14, 512) 262144
conv_pw_8_bn (BatchNormaliz (None, 14, 14, 512) 2048
ation)
conv_pw_8_relu (ReLU) (None, 14, 14, 512) 0
conv_dw_9 (DepthwiseConv2D) (None, 14, 14, 512) 4608
conv_dw_9_bn (BatchNormaliz (None, 14, 14, 512) 2048
ation)
conv_dw_9_relu (ReLU) (None, 14, 14, 512) 0
conv_pw_9 (Conv2D) (None, 14, 14, 512) 262144
conv_pw_9_bn (BatchNormaliz (None, 14, 14, 512) 2048
ation)
conv_pw_9_relu (ReLU) (None, 14, 14, 512) 0
conv_dw_10 (DepthwiseConv2D (None, 14, 14, 512) 4608
)
conv_dw_10_bn (BatchNormali (None, 14, 14, 512) 2048
zation)
conv_dw_10_relu (ReLU) (None, 14, 14, 512) 0
conv_pw_10 (Conv2D) (None, 14, 14, 512) 262144
conv_pw_10_bn (BatchNormali (None, 14, 14, 512) 2048
zation)
conv_pw_10_relu (ReLU) (None, 14, 14, 512) 0
conv_dw_11 (DepthwiseConv2D (None, 14, 14, 512) 4608
)
conv_dw_11_bn (BatchNormali (None, 14, 14, 512) 2048
zation)
conv_dw_11_relu (ReLU) (None, 14, 14, 512) 0
conv_pw_11 (Conv2D) (None, 14, 14, 512) 262144
conv_pw_11_bn (BatchNormali (None, 14, 14, 512) 2048
zation)
conv_pw_11_relu (ReLU) (None, 14, 14, 512) 0
conv_pad_12 (ZeroPadding2D) (None, 15, 15, 512) 0
conv_dw_12 (DepthwiseConv2D (None, 7, 7, 512) 4608
)
conv_dw_12_bn (BatchNormali (None, 7, 7, 512) 2048
zation)
conv_dw_12_relu (ReLU) (None, 7, 7, 512) 0
conv_pw_12 (Conv2D) (None, 7, 7, 1024) 524288
conv_pw_12_bn (BatchNormali (None, 7, 7, 1024) 4096
zation)
conv_pw_12_relu (ReLU) (None, 7, 7, 1024) 0
conv_dw_13 (DepthwiseConv2D (None, 7, 7, 1024) 9216
)
conv_dw_13_bn (BatchNormali (None, 7, 7, 1024) 4096
zation)
conv_dw_13_relu (ReLU) (None, 7, 7, 1024) 0
conv_pw_13 (Conv2D) (None, 7, 7, 1024) 1048576
conv_pw_13_bn (BatchNormali (None, 7, 7, 1024) 4096
zation)
conv_pw_13_relu (ReLU) (None, 7, 7, 1024) 0
=================================================================
Total params: 3,228,864
Trainable params: 3,206,976
Non-trainable params: 21,888
_________________________________________________________________
def create_model(trainable=True):
model = MobileNet(input_shape=(IMAGE_HEIGHT, IMAGE_WIDTH, 3), include_top=False, alpha=ALPHA, weights="imagenet")
for layer in model.layers:
layer.trainable = trainable
block00 = model.layers[0].input
block01 = model.get_layer("conv_pw_1_relu").output
block02 = model.get_layer("conv_pw_2_relu").output
block03 = model.get_layer("conv_pw_3_relu").output
block05 = model.get_layer("conv_pw_5_relu").output
block11 = model.get_layer("conv_pw_11_relu").output
block13 = model.get_layer("conv_pw_13_relu").output
decoderBlock = Concatenate()([UpSampling2D()(block13), block11])
decoderBlock = Concatenate()([UpSampling2D()(decoderBlock), block05])
decoderBlock = Concatenate()([UpSampling2D()(decoderBlock), block03])
decoderBlock = Concatenate()([UpSampling2D()(decoderBlock), block01])
decoderBlock = Concatenate()([UpSampling2D()(decoderBlock), block00])
decoderBlock = Conv2D(1, kernel_size=1, activation="sigmoid")(decoderBlock)
decoderBlock = Reshape((IMAGE_HEIGHT, IMAGE_WIDTH))(decoderBlock)
return Model(inputs=model.input, outputs=decoderBlock)
2.B.
def dice_coefficient(y_true, y_pred):
numerator = 2 * tf.reduce_sum(y_true * y_pred)
denominator = tf.reduce_sum(y_true + y_pred)
return numerator / (denominator + tf.keras.backend.epsilon())
def loss(y_true, y_pred):
return binary_crossentropy(y_true, y_pred) - log(dice_coefficient(y_true, y_pred) + epsilon())
2.C.
model = create_model(False)
model.summary()
optimizer = tf.keras.optimizers.Adam(lr=1e-4, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)
model.compile(loss=loss, optimizer = optimizer, metrics=[dice_coefficient])
Model: "model"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_2 (InputLayer) [(None, 224, 224, 3 0 []
)]
conv1 (Conv2D) (None, 112, 112, 32 864 ['input_2[0][0]']
)
conv1_bn (BatchNormalization) (None, 112, 112, 32 128 ['conv1[0][0]']
)
conv1_relu (ReLU) (None, 112, 112, 32 0 ['conv1_bn[0][0]']
)
conv_dw_1 (DepthwiseConv2D) (None, 112, 112, 32 288 ['conv1_relu[0][0]']
)
conv_dw_1_bn (BatchNormalizati (None, 112, 112, 32 128 ['conv_dw_1[0][0]']
on) )
conv_dw_1_relu (ReLU) (None, 112, 112, 32 0 ['conv_dw_1_bn[0][0]']
)
conv_pw_1 (Conv2D) (None, 112, 112, 64 2048 ['conv_dw_1_relu[0][0]']
)
conv_pw_1_bn (BatchNormalizati (None, 112, 112, 64 256 ['conv_pw_1[0][0]']
on) )
conv_pw_1_relu (ReLU) (None, 112, 112, 64 0 ['conv_pw_1_bn[0][0]']
)
conv_pad_2 (ZeroPadding2D) (None, 113, 113, 64 0 ['conv_pw_1_relu[0][0]']
)
conv_dw_2 (DepthwiseConv2D) (None, 56, 56, 64) 576 ['conv_pad_2[0][0]']
conv_dw_2_bn (BatchNormalizati (None, 56, 56, 64) 256 ['conv_dw_2[0][0]']
on)
conv_dw_2_relu (ReLU) (None, 56, 56, 64) 0 ['conv_dw_2_bn[0][0]']
conv_pw_2 (Conv2D) (None, 56, 56, 128) 8192 ['conv_dw_2_relu[0][0]']
conv_pw_2_bn (BatchNormalizati (None, 56, 56, 128) 512 ['conv_pw_2[0][0]']
on)
conv_pw_2_relu (ReLU) (None, 56, 56, 128) 0 ['conv_pw_2_bn[0][0]']
conv_dw_3 (DepthwiseConv2D) (None, 56, 56, 128) 1152 ['conv_pw_2_relu[0][0]']
conv_dw_3_bn (BatchNormalizati (None, 56, 56, 128) 512 ['conv_dw_3[0][0]']
on)
conv_dw_3_relu (ReLU) (None, 56, 56, 128) 0 ['conv_dw_3_bn[0][0]']
conv_pw_3 (Conv2D) (None, 56, 56, 128) 16384 ['conv_dw_3_relu[0][0]']
conv_pw_3_bn (BatchNormalizati (None, 56, 56, 128) 512 ['conv_pw_3[0][0]']
on)
conv_pw_3_relu (ReLU) (None, 56, 56, 128) 0 ['conv_pw_3_bn[0][0]']
conv_pad_4 (ZeroPadding2D) (None, 57, 57, 128) 0 ['conv_pw_3_relu[0][0]']
conv_dw_4 (DepthwiseConv2D) (None, 28, 28, 128) 1152 ['conv_pad_4[0][0]']
conv_dw_4_bn (BatchNormalizati (None, 28, 28, 128) 512 ['conv_dw_4[0][0]']
on)
conv_dw_4_relu (ReLU) (None, 28, 28, 128) 0 ['conv_dw_4_bn[0][0]']
conv_pw_4 (Conv2D) (None, 28, 28, 256) 32768 ['conv_dw_4_relu[0][0]']
conv_pw_4_bn (BatchNormalizati (None, 28, 28, 256) 1024 ['conv_pw_4[0][0]']
on)
conv_pw_4_relu (ReLU) (None, 28, 28, 256) 0 ['conv_pw_4_bn[0][0]']
conv_dw_5 (DepthwiseConv2D) (None, 28, 28, 256) 2304 ['conv_pw_4_relu[0][0]']
conv_dw_5_bn (BatchNormalizati (None, 28, 28, 256) 1024 ['conv_dw_5[0][0]']
on)
conv_dw_5_relu (ReLU) (None, 28, 28, 256) 0 ['conv_dw_5_bn[0][0]']
conv_pw_5 (Conv2D) (None, 28, 28, 256) 65536 ['conv_dw_5_relu[0][0]']
conv_pw_5_bn (BatchNormalizati (None, 28, 28, 256) 1024 ['conv_pw_5[0][0]']
on)
conv_pw_5_relu (ReLU) (None, 28, 28, 256) 0 ['conv_pw_5_bn[0][0]']
conv_pad_6 (ZeroPadding2D) (None, 29, 29, 256) 0 ['conv_pw_5_relu[0][0]']
conv_dw_6 (DepthwiseConv2D) (None, 14, 14, 256) 2304 ['conv_pad_6[0][0]']
conv_dw_6_bn (BatchNormalizati (None, 14, 14, 256) 1024 ['conv_dw_6[0][0]']
on)
conv_dw_6_relu (ReLU) (None, 14, 14, 256) 0 ['conv_dw_6_bn[0][0]']
conv_pw_6 (Conv2D) (None, 14, 14, 512) 131072 ['conv_dw_6_relu[0][0]']
conv_pw_6_bn (BatchNormalizati (None, 14, 14, 512) 2048 ['conv_pw_6[0][0]']
on)
conv_pw_6_relu (ReLU) (None, 14, 14, 512) 0 ['conv_pw_6_bn[0][0]']
conv_dw_7 (DepthwiseConv2D) (None, 14, 14, 512) 4608 ['conv_pw_6_relu[0][0]']
conv_dw_7_bn (BatchNormalizati (None, 14, 14, 512) 2048 ['conv_dw_7[0][0]']
on)
conv_dw_7_relu (ReLU) (None, 14, 14, 512) 0 ['conv_dw_7_bn[0][0]']
conv_pw_7 (Conv2D) (None, 14, 14, 512) 262144 ['conv_dw_7_relu[0][0]']
conv_pw_7_bn (BatchNormalizati (None, 14, 14, 512) 2048 ['conv_pw_7[0][0]']
on)
conv_pw_7_relu (ReLU) (None, 14, 14, 512) 0 ['conv_pw_7_bn[0][0]']
conv_dw_8 (DepthwiseConv2D) (None, 14, 14, 512) 4608 ['conv_pw_7_relu[0][0]']
conv_dw_8_bn (BatchNormalizati (None, 14, 14, 512) 2048 ['conv_dw_8[0][0]']
on)
conv_dw_8_relu (ReLU) (None, 14, 14, 512) 0 ['conv_dw_8_bn[0][0]']
conv_pw_8 (Conv2D) (None, 14, 14, 512) 262144 ['conv_dw_8_relu[0][0]']
conv_pw_8_bn (BatchNormalizati (None, 14, 14, 512) 2048 ['conv_pw_8[0][0]']
on)
conv_pw_8_relu (ReLU) (None, 14, 14, 512) 0 ['conv_pw_8_bn[0][0]']
conv_dw_9 (DepthwiseConv2D) (None, 14, 14, 512) 4608 ['conv_pw_8_relu[0][0]']
conv_dw_9_bn (BatchNormalizati (None, 14, 14, 512) 2048 ['conv_dw_9[0][0]']
on)
conv_dw_9_relu (ReLU) (None, 14, 14, 512) 0 ['conv_dw_9_bn[0][0]']
conv_pw_9 (Conv2D) (None, 14, 14, 512) 262144 ['conv_dw_9_relu[0][0]']
conv_pw_9_bn (BatchNormalizati (None, 14, 14, 512) 2048 ['conv_pw_9[0][0]']
on)
conv_pw_9_relu (ReLU) (None, 14, 14, 512) 0 ['conv_pw_9_bn[0][0]']
conv_dw_10 (DepthwiseConv2D) (None, 14, 14, 512) 4608 ['conv_pw_9_relu[0][0]']
conv_dw_10_bn (BatchNormalizat (None, 14, 14, 512) 2048 ['conv_dw_10[0][0]']
ion)
conv_dw_10_relu (ReLU) (None, 14, 14, 512) 0 ['conv_dw_10_bn[0][0]']
conv_pw_10 (Conv2D) (None, 14, 14, 512) 262144 ['conv_dw_10_relu[0][0]']
conv_pw_10_bn (BatchNormalizat (None, 14, 14, 512) 2048 ['conv_pw_10[0][0]']
ion)
conv_pw_10_relu (ReLU) (None, 14, 14, 512) 0 ['conv_pw_10_bn[0][0]']
conv_dw_11 (DepthwiseConv2D) (None, 14, 14, 512) 4608 ['conv_pw_10_relu[0][0]']
conv_dw_11_bn (BatchNormalizat (None, 14, 14, 512) 2048 ['conv_dw_11[0][0]']
ion)
conv_dw_11_relu (ReLU) (None, 14, 14, 512) 0 ['conv_dw_11_bn[0][0]']
conv_pw_11 (Conv2D) (None, 14, 14, 512) 262144 ['conv_dw_11_relu[0][0]']
conv_pw_11_bn (BatchNormalizat (None, 14, 14, 512) 2048 ['conv_pw_11[0][0]']
ion)
conv_pw_11_relu (ReLU) (None, 14, 14, 512) 0 ['conv_pw_11_bn[0][0]']
conv_pad_12 (ZeroPadding2D) (None, 15, 15, 512) 0 ['conv_pw_11_relu[0][0]']
conv_dw_12 (DepthwiseConv2D) (None, 7, 7, 512) 4608 ['conv_pad_12[0][0]']
conv_dw_12_bn (BatchNormalizat (None, 7, 7, 512) 2048 ['conv_dw_12[0][0]']
ion)
conv_dw_12_relu (ReLU) (None, 7, 7, 512) 0 ['conv_dw_12_bn[0][0]']
conv_pw_12 (Conv2D) (None, 7, 7, 1024) 524288 ['conv_dw_12_relu[0][0]']
conv_pw_12_bn (BatchNormalizat (None, 7, 7, 1024) 4096 ['conv_pw_12[0][0]']
ion)
conv_pw_12_relu (ReLU) (None, 7, 7, 1024) 0 ['conv_pw_12_bn[0][0]']
conv_dw_13 (DepthwiseConv2D) (None, 7, 7, 1024) 9216 ['conv_pw_12_relu[0][0]']
conv_dw_13_bn (BatchNormalizat (None, 7, 7, 1024) 4096 ['conv_dw_13[0][0]']
ion)
conv_dw_13_relu (ReLU) (None, 7, 7, 1024) 0 ['conv_dw_13_bn[0][0]']
conv_pw_13 (Conv2D) (None, 7, 7, 1024) 1048576 ['conv_dw_13_relu[0][0]']
conv_pw_13_bn (BatchNormalizat (None, 7, 7, 1024) 4096 ['conv_pw_13[0][0]']
ion)
conv_pw_13_relu (ReLU) (None, 7, 7, 1024) 0 ['conv_pw_13_bn[0][0]']
up_sampling2d (UpSampling2D) (None, 14, 14, 1024 0 ['conv_pw_13_relu[0][0]']
)
concatenate (Concatenate) (None, 14, 14, 1536 0 ['up_sampling2d[0][0]',
) 'conv_pw_11_relu[0][0]']
up_sampling2d_1 (UpSampling2D) (None, 28, 28, 1536 0 ['concatenate[0][0]']
)
concatenate_1 (Concatenate) (None, 28, 28, 1792 0 ['up_sampling2d_1[0][0]',
) 'conv_pw_5_relu[0][0]']
up_sampling2d_2 (UpSampling2D) (None, 56, 56, 1792 0 ['concatenate_1[0][0]']
)
concatenate_2 (Concatenate) (None, 56, 56, 1920 0 ['up_sampling2d_2[0][0]',
) 'conv_pw_3_relu[0][0]']
up_sampling2d_3 (UpSampling2D) (None, 112, 112, 19 0 ['concatenate_2[0][0]']
20)
concatenate_3 (Concatenate) (None, 112, 112, 19 0 ['up_sampling2d_3[0][0]',
84) 'conv_pw_1_relu[0][0]']
up_sampling2d_4 (UpSampling2D) (None, 224, 224, 19 0 ['concatenate_3[0][0]']
84)
concatenate_4 (Concatenate) (None, 224, 224, 19 0 ['up_sampling2d_4[0][0]',
87) 'input_2[0][0]']
conv2d (Conv2D) (None, 224, 224, 1) 1988 ['concatenate_4[0][0]']
reshape (Reshape) (None, 224, 224) 0 ['conv2d[0][0]']
==================================================================================================
Total params: 3,230,852
Trainable params: 1,988
Non-trainable params: 3,228,864
__________________________________________________________________________________________________
checkpoint = ModelCheckpoint("moblenet_face_detection.h5", monitor="loss", verbose=1, save_best_only=True,
save_weights_only=True, mode="min", save_freq=1)
stop = EarlyStopping(monitor="loss", patience=5, mode="min")
reduce_lr = ReduceLROnPlateau(monitor="loss", factor=0.2, patience=1, min_lr=1e-6, verbose=1, mode="min")
valid_steps = len(X_val)//BATCH_SIZE
model.fit(X_train, y_train, validation_data=(X_val, y_val), validation_steps=valid_steps, epochs=5, verbose=1, batch_size=BATCH_SIZE, callbacks=[checkpoint,reduce_lr,stop])
Epoch 1/5 Epoch 1: loss improved from inf to 1.89876, saving model to moblenet_face_detection.h5 1/134 [..............................] - ETA: 24:49 - loss: 1.8988 - dice_coefficient: 0.2912 Epoch 1: loss improved from 1.89876 to 1.80403, saving model to moblenet_face_detection.h5 2/134 [..............................] - ETA: 31s - loss: 1.8040 - dice_coefficient: 0.3313 Epoch 1: loss improved from 1.80403 to 1.74724, saving model to moblenet_face_detection.h5 3/134 [..............................] - ETA: 32s - loss: 1.7472 - dice_coefficient: 0.3441 Epoch 1: loss did not improve from 1.74724 4/134 [..............................] - ETA: 26s - loss: 1.9427 - dice_coefficient: 0.3029 Epoch 1: loss did not improve from 1.74724 5/134 [>.............................] - ETA: 22s - loss: 1.9195 - dice_coefficient: 0.3001 Epoch 1: loss did not improve from 1.74724 6/134 [>.............................] - ETA: 20s - loss: 1.9296 - dice_coefficient: 0.2946 Epoch 1: loss did not improve from 1.74724 7/134 [>.............................] - ETA: 19s - loss: 1.9472 - dice_coefficient: 0.2890 Epoch 1: loss did not improve from 1.74724 8/134 [>.............................] - ETA: 18s - loss: 2.0443 - dice_coefficient: 0.2662 Epoch 1: loss did not improve from 1.74724 9/134 [=>............................] - ETA: 17s - loss: 2.0589 - dice_coefficient: 0.2602 Epoch 1: loss did not improve from 1.74724 10/134 [=>............................] - ETA: 17s - loss: 2.0561 - dice_coefficient: 0.2571 Epoch 1: loss did not improve from 1.74724 11/134 [=>............................] - ETA: 16s - loss: 2.1584 - dice_coefficient: 0.2405 Epoch 1: loss did not improve from 1.74724 12/134 [=>............................] - ETA: 16s - loss: 2.1395 - dice_coefficient: 0.2423 Epoch 1: loss did not improve from 1.74724 13/134 [=>............................] - ETA: 15s - loss: 2.1231 - dice_coefficient: 0.2444 Epoch 1: loss did not improve from 1.74724 14/134 [==>...........................] - ETA: 15s - loss: 2.1666 - dice_coefficient: 0.2348 Epoch 1: loss did not improve from 1.74724 15/134 [==>...........................] - ETA: 14s - loss: 2.1419 - dice_coefficient: 0.2397 Epoch 1: loss did not improve from 1.74724 16/134 [==>...........................] - ETA: 14s - loss: 2.1446 - dice_coefficient: 0.2372 Epoch 1: loss did not improve from 1.74724 17/134 [==>...........................] - ETA: 14s - loss: 2.1376 - dice_coefficient: 0.2387 Epoch 1: loss did not improve from 1.74724 18/134 [===>..........................] - ETA: 14s - loss: 2.1526 - dice_coefficient: 0.2357 Epoch 1: loss did not improve from 1.74724 19/134 [===>..........................] - ETA: 13s - loss: 2.1747 - dice_coefficient: 0.2307 Epoch 1: loss did not improve from 1.74724 20/134 [===>..........................] - ETA: 13s - loss: 2.1578 - dice_coefficient: 0.2314 Epoch 1: loss did not improve from 1.74724 21/134 [===>..........................] - ETA: 13s - loss: 2.1280 - dice_coefficient: 0.2387 Epoch 1: loss did not improve from 1.74724 22/134 [===>..........................] - ETA: 13s - loss: 2.1229 - dice_coefficient: 0.2375 Epoch 1: loss did not improve from 1.74724 23/134 [====>.........................] - ETA: 13s - loss: 2.1012 - dice_coefficient: 0.2421 Epoch 1: loss did not improve from 1.74724 24/134 [====>.........................] - ETA: 12s - loss: 2.1150 - dice_coefficient: 0.2382 Epoch 1: loss did not improve from 1.74724 25/134 [====>.........................] - ETA: 12s - loss: 2.1122 - dice_coefficient: 0.2377 Epoch 1: loss did not improve from 1.74724 26/134 [====>.........................] - ETA: 12s - loss: 2.1104 - dice_coefficient: 0.2368 Epoch 1: loss did not improve from 1.74724 27/134 [=====>........................] - ETA: 12s - loss: 2.1142 - dice_coefficient: 0.2353 Epoch 1: loss did not improve from 1.74724 28/134 [=====>........................] - ETA: 12s - loss: 2.0900 - dice_coefficient: 0.2388 Epoch 1: loss did not improve from 1.74724 29/134 [=====>........................] - ETA: 12s - loss: 2.1228 - dice_coefficient: 0.2331 Epoch 1: loss did not improve from 1.74724 30/134 [=====>........................] - ETA: 11s - loss: 2.1001 - dice_coefficient: 0.2373 Epoch 1: loss did not improve from 1.74724 31/134 [=====>........................] - ETA: 11s - loss: 2.0784 - dice_coefficient: 0.2419 Epoch 1: loss did not improve from 1.74724 32/134 [======>.......................] - ETA: 11s - loss: 2.0896 - dice_coefficient: 0.2383 Epoch 1: loss did not improve from 1.74724 33/134 [======>.......................] - ETA: 11s - loss: 2.1029 - dice_coefficient: 0.2344 Epoch 1: loss did not improve from 1.74724 34/134 [======>.......................] - ETA: 11s - loss: 2.0873 - dice_coefficient: 0.2378 Epoch 1: loss did not improve from 1.74724 35/134 [======>.......................] - ETA: 11s - loss: 2.0830 - dice_coefficient: 0.2372 Epoch 1: loss did not improve from 1.74724 36/134 [=======>......................] - ETA: 11s - loss: 2.0801 - dice_coefficient: 0.2363 Epoch 1: loss did not improve from 1.74724 37/134 [=======>......................] - ETA: 10s - loss: 2.0844 - dice_coefficient: 0.2354 Epoch 1: loss did not improve from 1.74724 38/134 [=======>......................] - ETA: 10s - loss: 2.0603 - dice_coefficient: 0.2422 Epoch 1: loss did not improve from 1.74724 39/134 [=======>......................] - ETA: 10s - loss: 2.0577 - dice_coefficient: 0.2431 Epoch 1: loss did not improve from 1.74724 40/134 [=======>......................] - ETA: 10s - loss: 2.0507 - dice_coefficient: 0.2433 Epoch 1: loss did not improve from 1.74724 41/134 [========>.....................] - ETA: 10s - loss: 2.0467 - dice_coefficient: 0.2443 Epoch 1: loss did not improve from 1.74724 42/134 [========>.....................] - ETA: 10s - loss: 2.0589 - dice_coefficient: 0.2412 Epoch 1: loss did not improve from 1.74724 43/134 [========>.....................] - ETA: 10s - loss: 2.0438 - dice_coefficient: 0.2443 Epoch 1: loss did not improve from 1.74724 44/134 [========>.....................] - ETA: 10s - loss: 2.0341 - dice_coefficient: 0.2451 Epoch 1: loss did not improve from 1.74724 45/134 [=========>....................] - ETA: 9s - loss: 2.0384 - dice_coefficient: 0.2436 Epoch 1: loss did not improve from 1.74724 46/134 [=========>....................] - ETA: 9s - loss: 2.0366 - dice_coefficient: 0.2430 Epoch 1: loss did not improve from 1.74724 47/134 [=========>....................] - ETA: 9s - loss: 2.0241 - dice_coefficient: 0.2455 Epoch 1: loss did not improve from 1.74724 48/134 [=========>....................] - ETA: 9s - loss: 2.0167 - dice_coefficient: 0.2461 Epoch 1: loss did not improve from 1.74724 49/134 [=========>....................] - ETA: 9s - loss: 2.0151 - dice_coefficient: 0.2451 Epoch 1: loss did not improve from 1.74724 50/134 [==========>...................] - ETA: 9s - loss: 2.0033 - dice_coefficient: 0.2486 Epoch 1: loss did not improve from 1.74724 51/134 [==========>...................] - ETA: 9s - loss: 2.0040 - dice_coefficient: 0.2491 Epoch 1: loss did not improve from 1.74724 52/134 [==========>...................] - ETA: 9s - loss: 1.9972 - dice_coefficient: 0.2496 Epoch 1: loss did not improve from 1.74724 53/134 [==========>...................] - ETA: 8s - loss: 1.9940 - dice_coefficient: 0.2501 Epoch 1: loss did not improve from 1.74724 54/134 [===========>..................] - ETA: 8s - loss: 1.9842 - dice_coefficient: 0.2527 Epoch 1: loss did not improve from 1.74724 55/134 [===========>..................] - ETA: 8s - loss: 1.9858 - dice_coefficient: 0.2517 Epoch 1: loss did not improve from 1.74724 56/134 [===========>..................] - ETA: 8s - loss: 1.9722 - dice_coefficient: 0.2557 Epoch 1: loss did not improve from 1.74724 57/134 [===========>..................] - ETA: 8s - loss: 1.9699 - dice_coefficient: 0.2550 Epoch 1: loss did not improve from 1.74724 58/134 [===========>..................] - ETA: 8s - loss: 1.9690 - dice_coefficient: 0.2547 Epoch 1: loss did not improve from 1.74724 59/134 [============>.................] - ETA: 8s - loss: 1.9654 - dice_coefficient: 0.2551 Epoch 1: loss did not improve from 1.74724 60/134 [============>.................] - ETA: 8s - loss: 1.9598 - dice_coefficient: 0.2557 Epoch 1: loss did not improve from 1.74724 61/134 [============>.................] - ETA: 7s - loss: 1.9536 - dice_coefficient: 0.2563 Epoch 1: loss did not improve from 1.74724 62/134 [============>.................] - ETA: 7s - loss: 1.9583 - dice_coefficient: 0.2550 Epoch 1: loss did not improve from 1.74724 63/134 [=============>................] - ETA: 7s - loss: 1.9534 - dice_coefficient: 0.2554 Epoch 1: loss did not improve from 1.74724 64/134 [=============>................] - ETA: 7s - loss: 1.9436 - dice_coefficient: 0.2577 Epoch 1: loss did not improve from 1.74724 65/134 [=============>................] - ETA: 7s - loss: 1.9459 - dice_coefficient: 0.2563 Epoch 1: loss did not improve from 1.74724 66/134 [=============>................] - ETA: 7s - loss: 1.9436 - dice_coefficient: 0.2563 Epoch 1: loss did not improve from 1.74724 67/134 [==============>...............] - ETA: 7s - loss: 1.9558 - dice_coefficient: 0.2537 Epoch 1: loss did not improve from 1.74724 68/134 [==============>...............] - ETA: 7s - loss: 1.9514 - dice_coefficient: 0.2547 Epoch 1: loss did not improve from 1.74724 69/134 [==============>...............] - ETA: 7s - loss: 1.9464 - dice_coefficient: 0.2556 Epoch 1: loss did not improve from 1.74724 70/134 [==============>...............] - ETA: 6s - loss: 1.9377 - dice_coefficient: 0.2579 Epoch 1: loss did not improve from 1.74724 71/134 [==============>...............] - ETA: 6s - loss: 1.9298 - dice_coefficient: 0.2595 Epoch 1: loss did not improve from 1.74724 72/134 [===============>..............] - ETA: 6s - loss: 1.9291 - dice_coefficient: 0.2586 Epoch 1: loss did not improve from 1.74724 73/134 [===============>..............] - ETA: 6s - loss: 1.9292 - dice_coefficient: 0.2584 Epoch 1: loss did not improve from 1.74724 74/134 [===============>..............] - ETA: 6s - loss: 1.9272 - dice_coefficient: 0.2583 Epoch 1: loss did not improve from 1.74724 75/134 [===============>..............] - ETA: 6s - loss: 1.9233 - dice_coefficient: 0.2587 Epoch 1: loss did not improve from 1.74724 76/134 [================>.............] - ETA: 6s - loss: 1.9328 - dice_coefficient: 0.2565 Epoch 1: loss did not improve from 1.74724 77/134 [================>.............] - ETA: 6s - loss: 1.9283 - dice_coefficient: 0.2571 Epoch 1: loss did not improve from 1.74724 78/134 [================>.............] - ETA: 6s - loss: 1.9198 - dice_coefficient: 0.2587 Epoch 1: loss did not improve from 1.74724 79/134 [================>.............] - ETA: 5s - loss: 1.9222 - dice_coefficient: 0.2577 Epoch 1: loss did not improve from 1.74724 80/134 [================>.............] - ETA: 5s - loss: 1.9174 - dice_coefficient: 0.2588 Epoch 1: loss did not improve from 1.74724 81/134 [=================>............] - ETA: 5s - loss: 1.9141 - dice_coefficient: 0.2588 Epoch 1: loss did not improve from 1.74724 82/134 [=================>............] - ETA: 5s - loss: 1.9048 - dice_coefficient: 0.2614 Epoch 1: loss did not improve from 1.74724 83/134 [=================>............] - ETA: 5s - loss: 1.8979 - dice_coefficient: 0.2627 Epoch 1: loss did not improve from 1.74724 84/134 [=================>............] - ETA: 5s - loss: 1.8909 - dice_coefficient: 0.2648 Epoch 1: loss did not improve from 1.74724 85/134 [==================>...........] - ETA: 5s - loss: 1.8888 - dice_coefficient: 0.2647 Epoch 1: loss did not improve from 1.74724 86/134 [==================>...........] - ETA: 5s - loss: 1.8824 - dice_coefficient: 0.2658 Epoch 1: loss did not improve from 1.74724 87/134 [==================>...........] - ETA: 5s - loss: 1.8765 - dice_coefficient: 0.2671 Epoch 1: loss did not improve from 1.74724 88/134 [==================>...........] - ETA: 4s - loss: 1.8700 - dice_coefficient: 0.2689 Epoch 1: loss did not improve from 1.74724 89/134 [==================>...........] - ETA: 4s - loss: 1.8707 - dice_coefficient: 0.2682 Epoch 1: loss did not improve from 1.74724 90/134 [===================>..........] - ETA: 4s - loss: 1.8679 - dice_coefficient: 0.2679 Epoch 1: loss did not improve from 1.74724 91/134 [===================>..........] - ETA: 4s - loss: 1.8692 - dice_coefficient: 0.2670 Epoch 1: loss did not improve from 1.74724 92/134 [===================>..........] - ETA: 4s - loss: 1.8644 - dice_coefficient: 0.2674 Epoch 1: loss did not improve from 1.74724 93/134 [===================>..........] - ETA: 4s - loss: 1.8609 - dice_coefficient: 0.2678 Epoch 1: loss did not improve from 1.74724 94/134 [====================>.........] - ETA: 4s - loss: 1.8554 - dice_coefficient: 0.2688 Epoch 1: loss did not improve from 1.74724 95/134 [====================>.........] - ETA: 4s - loss: 1.8536 - dice_coefficient: 0.2684 Epoch 1: loss did not improve from 1.74724 96/134 [====================>.........] - ETA: 4s - loss: 1.8517 - dice_coefficient: 0.2683 Epoch 1: loss did not improve from 1.74724 97/134 [====================>.........] - ETA: 3s - loss: 1.8465 - dice_coefficient: 0.2697 Epoch 1: loss did not improve from 1.74724 98/134 [====================>.........] - ETA: 3s - loss: 1.8444 - dice_coefficient: 0.2707 Epoch 1: loss did not improve from 1.74724 99/134 [=====================>........] - ETA: 3s - loss: 1.8402 - dice_coefficient: 0.2713 Epoch 1: loss did not improve from 1.74724 100/134 [=====================>........] - ETA: 3s - loss: 1.8361 - dice_coefficient: 0.2718 Epoch 1: loss did not improve from 1.74724 101/134 [=====================>........] - ETA: 3s - loss: 1.8329 - dice_coefficient: 0.2720 Epoch 1: loss did not improve from 1.74724 102/134 [=====================>........] - ETA: 3s - loss: 1.8293 - dice_coefficient: 0.2725 Epoch 1: loss did not improve from 1.74724 103/134 [======================>.......] - ETA: 3s - loss: 1.8351 - dice_coefficient: 0.2709 Epoch 1: loss did not improve from 1.74724 104/134 [======================>.......] - ETA: 3s - loss: 1.8305 - dice_coefficient: 0.2715 Epoch 1: loss did not improve from 1.74724 105/134 [======================>.......] - ETA: 3s - loss: 1.8295 - dice_coefficient: 0.2713 Epoch 1: loss did not improve from 1.74724 106/134 [======================>.......] - ETA: 3s - loss: 1.8274 - dice_coefficient: 0.2725 Epoch 1: loss did not improve from 1.74724 107/134 [======================>.......] - ETA: 2s - loss: 1.8251 - dice_coefficient: 0.2726 Epoch 1: loss did not improve from 1.74724 108/134 [=======================>......] - ETA: 2s - loss: 1.8193 - dice_coefficient: 0.2740 Epoch 1: loss did not improve from 1.74724 109/134 [=======================>......] - ETA: 2s - loss: 1.8154 - dice_coefficient: 0.2749 Epoch 1: loss did not improve from 1.74724 110/134 [=======================>......] - ETA: 2s - loss: 1.8131 - dice_coefficient: 0.2751 Epoch 1: loss did not improve from 1.74724 111/134 [=======================>......] - ETA: 2s - loss: 1.8086 - dice_coefficient: 0.2760 Epoch 1: loss did not improve from 1.74724 112/134 [========================>.....] - ETA: 2s - loss: 1.8084 - dice_coefficient: 0.2752 Epoch 1: loss did not improve from 1.74724 113/134 [========================>.....] - ETA: 2s - loss: 1.8114 - dice_coefficient: 0.2740 Epoch 1: loss did not improve from 1.74724 114/134 [========================>.....] - ETA: 2s - loss: 1.8086 - dice_coefficient: 0.2745 Epoch 1: loss did not improve from 1.74724 115/134 [========================>.....] - ETA: 2s - loss: 1.8123 - dice_coefficient: 0.2734 Epoch 1: loss did not improve from 1.74724 116/134 [========================>.....] - ETA: 1s - loss: 1.8102 - dice_coefficient: 0.2737 Epoch 1: loss did not improve from 1.74724 117/134 [=========================>....] - ETA: 1s - loss: 1.8068 - dice_coefficient: 0.2748 Epoch 1: loss did not improve from 1.74724 118/134 [=========================>....] - ETA: 1s - loss: 1.8014 - dice_coefficient: 0.2763 Epoch 1: loss did not improve from 1.74724 119/134 [=========================>....] - ETA: 1s - loss: 1.7975 - dice_coefficient: 0.2769 Epoch 1: loss did not improve from 1.74724 120/134 [=========================>....] - ETA: 1s - loss: 1.7921 - dice_coefficient: 0.2787 Epoch 1: loss did not improve from 1.74724 121/134 [==========================>...] - ETA: 1s - loss: 1.7923 - dice_coefficient: 0.2783 Epoch 1: loss did not improve from 1.74724 122/134 [==========================>...] - ETA: 1s - loss: 1.7880 - dice_coefficient: 0.2793 Epoch 1: loss did not improve from 1.74724 123/134 [==========================>...] - ETA: 1s - loss: 1.7854 - dice_coefficient: 0.2799 Epoch 1: loss did not improve from 1.74724 124/134 [==========================>...] - ETA: 1s - loss: 1.7796 - dice_coefficient: 0.2812 Epoch 1: loss did not improve from 1.74724 125/134 [==========================>...] - ETA: 0s - loss: 1.7741 - dice_coefficient: 0.2827 Epoch 1: loss did not improve from 1.74724 126/134 [===========================>..] - ETA: 0s - loss: 1.7731 - dice_coefficient: 0.2826 Epoch 1: loss did not improve from 1.74724 127/134 [===========================>..] - ETA: 0s - loss: 1.7720 - dice_coefficient: 0.2825 Epoch 1: loss did not improve from 1.74724 128/134 [===========================>..] - ETA: 0s - loss: 1.7701 - dice_coefficient: 0.2827 Epoch 1: loss did not improve from 1.74724 129/134 [===========================>..] - ETA: 0s - loss: 1.7650 - dice_coefficient: 0.2843 Epoch 1: loss did not improve from 1.74724 130/134 [============================>.] - ETA: 0s - loss: 1.7610 - dice_coefficient: 0.2856 Epoch 1: loss did not improve from 1.74724 131/134 [============================>.] - ETA: 0s - loss: 1.7597 - dice_coefficient: 0.2859 Epoch 1: loss did not improve from 1.74724 132/134 [============================>.] - ETA: 0s - loss: 1.7584 - dice_coefficient: 0.2856 Epoch 1: loss did not improve from 1.74724 133/134 [============================>.] - ETA: 0s - loss: 1.7575 - dice_coefficient: 0.2854 Epoch 1: loss did not improve from 1.74724 134/134 [==============================] - 26s 110ms/step - loss: 1.7560 - dice_coefficient: 0.2865 - lr: 1.0000e-04 Epoch 2/5 Epoch 2: loss improved from 1.74724 to 1.22533, saving model to moblenet_face_detection.h5 1/134 [..............................] - ETA: 31s - loss: 1.2253 - dice_coefficient: 0.4227 Epoch 2: loss did not improve from 1.22533 2/134 [..............................] - ETA: 14s - loss: 1.7160 - dice_coefficient: 0.2829 Epoch 2: loss did not improve from 1.22533 3/134 [..............................] - ETA: 14s - loss: 1.5230 - dice_coefficient: 0.3330 Epoch 2: loss did not improve from 1.22533 4/134 [..............................] - ETA: 13s - loss: 1.5439 - dice_coefficient: 0.3178 Epoch 2: loss did not improve from 1.22533 5/134 [>.............................] - ETA: 13s - loss: 1.4540 - dice_coefficient: 0.3461 Epoch 2: loss did not improve from 1.22533 6/134 [>.............................] - ETA: 13s - loss: 1.4380 - dice_coefficient: 0.3515 Epoch 2: loss did not improve from 1.22533 7/134 [>.............................] - ETA: 13s - loss: 1.4699 - dice_coefficient: 0.3377 Epoch 2: loss did not improve from 1.22533 8/134 [>.............................] - ETA: 13s - loss: 1.4185 - dice_coefficient: 0.3623 Epoch 2: loss did not improve from 1.22533 9/134 [=>............................] - ETA: 13s - loss: 1.3853 - dice_coefficient: 0.3727 Epoch 2: loss did not improve from 1.22533 10/134 [=>............................] - ETA: 13s - loss: 1.3552 - dice_coefficient: 0.3849 Epoch 2: loss did not improve from 1.22533 11/134 [=>............................] - ETA: 12s - loss: 1.3759 - dice_coefficient: 0.3754 Epoch 2: loss did not improve from 1.22533 12/134 [=>............................] - ETA: 12s - loss: 1.4277 - dice_coefficient: 0.3602 Epoch 2: loss did not improve from 1.22533 13/134 [=>............................] - ETA: 12s - loss: 1.4013 - dice_coefficient: 0.3651 Epoch 2: loss did not improve from 1.22533 14/134 [==>...........................] - ETA: 12s - loss: 1.4227 - dice_coefficient: 0.3568 Epoch 2: loss did not improve from 1.22533 15/134 [==>...........................] - ETA: 12s - loss: 1.4076 - dice_coefficient: 0.3605 Epoch 2: loss did not improve from 1.22533 16/134 [==>...........................] - ETA: 12s - loss: 1.4021 - dice_coefficient: 0.3633 Epoch 2: loss did not improve from 1.22533 17/134 [==>...........................] - ETA: 12s - loss: 1.3848 - dice_coefficient: 0.3677 Epoch 2: loss did not improve from 1.22533 18/134 [===>..........................] - ETA: 12s - loss: 1.4046 - dice_coefficient: 0.3593 Epoch 2: loss did not improve from 1.22533 19/134 [===>..........................] - ETA: 12s - loss: 1.4197 - dice_coefficient: 0.3521 Epoch 2: loss did not improve from 1.22533 20/134 [===>..........................] - ETA: 12s - loss: 1.4427 - dice_coefficient: 0.3447 Epoch 2: loss did not improve from 1.22533 21/134 [===>..........................] - ETA: 11s - loss: 1.4271 - dice_coefficient: 0.3509 Epoch 2: loss did not improve from 1.22533 22/134 [===>..........................] - ETA: 11s - loss: 1.4088 - dice_coefficient: 0.3553 Epoch 2: loss did not improve from 1.22533 23/134 [====>.........................] - ETA: 11s - loss: 1.4046 - dice_coefficient: 0.3555 Epoch 2: loss did not improve from 1.22533 24/134 [====>.........................] - ETA: 11s - loss: 1.4201 - dice_coefficient: 0.3505 Epoch 2: loss did not improve from 1.22533 25/134 [====>.........................] - ETA: 11s - loss: 1.4097 - dice_coefficient: 0.3537 Epoch 2: loss did not improve from 1.22533 26/134 [====>.........................] - ETA: 11s - loss: 1.4004 - dice_coefficient: 0.3563 Epoch 2: loss did not improve from 1.22533 27/134 [=====>........................] - ETA: 11s - loss: 1.4076 - dice_coefficient: 0.3533 Epoch 2: loss did not improve from 1.22533 28/134 [=====>........................] - ETA: 11s - loss: 1.3923 - dice_coefficient: 0.3575 Epoch 2: loss did not improve from 1.22533 29/134 [=====>........................] - ETA: 11s - loss: 1.3893 - dice_coefficient: 0.3582 Epoch 2: loss did not improve from 1.22533 30/134 [=====>........................] - ETA: 10s - loss: 1.3928 - dice_coefficient: 0.3565 Epoch 2: loss did not improve from 1.22533 31/134 [=====>........................] - ETA: 10s - loss: 1.3922 - dice_coefficient: 0.3595 Epoch 2: loss did not improve from 1.22533 32/134 [======>.......................] - ETA: 10s - loss: 1.3941 - dice_coefficient: 0.3583 Epoch 2: loss did not improve from 1.22533 33/134 [======>.......................] - ETA: 10s - loss: 1.4071 - dice_coefficient: 0.3540 Epoch 2: loss did not improve from 1.22533 34/134 [======>.......................] - ETA: 10s - loss: 1.4087 - dice_coefficient: 0.3527 Epoch 2: loss did not improve from 1.22533 35/134 [======>.......................] - ETA: 10s - loss: 1.4166 - dice_coefficient: 0.3502 Epoch 2: loss did not improve from 1.22533 36/134 [=======>......................] - ETA: 10s - loss: 1.4152 - dice_coefficient: 0.3503 Epoch 2: loss did not improve from 1.22533 37/134 [=======>......................] - ETA: 10s - loss: 1.4144 - dice_coefficient: 0.3499 Epoch 2: loss did not improve from 1.22533 38/134 [=======>......................] - ETA: 10s - loss: 1.4063 - dice_coefficient: 0.3513 Epoch 2: loss did not improve from 1.22533 39/134 [=======>......................] - ETA: 9s - loss: 1.3891 - dice_coefficient: 0.3576 Epoch 2: loss did not improve from 1.22533 40/134 [=======>......................] - ETA: 9s - loss: 1.3886 - dice_coefficient: 0.3582 Epoch 2: loss did not improve from 1.22533 41/134 [========>.....................] - ETA: 9s - loss: 1.3829 - dice_coefficient: 0.3593 Epoch 2: loss did not improve from 1.22533 42/134 [========>.....................] - ETA: 9s - loss: 1.3800 - dice_coefficient: 0.3602 Epoch 2: loss did not improve from 1.22533 43/134 [========>.....................] - ETA: 9s - loss: 1.3735 - dice_coefficient: 0.3623 Epoch 2: loss did not improve from 1.22533 44/134 [========>.....................] - ETA: 9s - loss: 1.3701 - dice_coefficient: 0.3627 Epoch 2: loss did not improve from 1.22533 45/134 [=========>....................] - ETA: 9s - loss: 1.3641 - dice_coefficient: 0.3641 Epoch 2: loss did not improve from 1.22533 46/134 [=========>....................] - ETA: 9s - loss: 1.3809 - dice_coefficient: 0.3591 Epoch 2: loss did not improve from 1.22533 47/134 [=========>....................] - ETA: 9s - loss: 1.3771 - dice_coefficient: 0.3595 Epoch 2: loss did not improve from 1.22533 48/134 [=========>....................] - ETA: 9s - loss: 1.3718 - dice_coefficient: 0.3627 Epoch 2: loss did not improve from 1.22533 49/134 [=========>....................] - ETA: 8s - loss: 1.3704 - dice_coefficient: 0.3649 Epoch 2: loss did not improve from 1.22533 50/134 [==========>...................] - ETA: 8s - loss: 1.3674 - dice_coefficient: 0.3651 Epoch 2: loss did not improve from 1.22533 51/134 [==========>...................] - ETA: 8s - loss: 1.3694 - dice_coefficient: 0.3639 Epoch 2: loss did not improve from 1.22533 52/134 [==========>...................] - ETA: 8s - loss: 1.3648 - dice_coefficient: 0.3660 Epoch 2: loss did not improve from 1.22533 53/134 [==========>...................] - ETA: 8s - loss: 1.3581 - dice_coefficient: 0.3686 Epoch 2: loss did not improve from 1.22533 54/134 [===========>..................] - ETA: 8s - loss: 1.3598 - dice_coefficient: 0.3679 Epoch 2: loss did not improve from 1.22533 55/134 [===========>..................] - ETA: 8s - loss: 1.3546 - dice_coefficient: 0.3699 Epoch 2: loss did not improve from 1.22533 56/134 [===========>..................] - ETA: 8s - loss: 1.3553 - dice_coefficient: 0.3694 Epoch 2: loss did not improve from 1.22533 57/134 [===========>..................] - ETA: 8s - loss: 1.3487 - dice_coefficient: 0.3723 Epoch 2: loss did not improve from 1.22533 58/134 [===========>..................] - ETA: 8s - loss: 1.3430 - dice_coefficient: 0.3743 Epoch 2: loss did not improve from 1.22533 59/134 [============>.................] - ETA: 7s - loss: 1.3403 - dice_coefficient: 0.3739 Epoch 2: loss did not improve from 1.22533 60/134 [============>.................] - ETA: 7s - loss: 1.3339 - dice_coefficient: 0.3760 Epoch 2: loss did not improve from 1.22533 61/134 [============>.................] - ETA: 7s - loss: 1.3337 - dice_coefficient: 0.3762 Epoch 2: loss did not improve from 1.22533 62/134 [============>.................] - ETA: 7s - loss: 1.3289 - dice_coefficient: 0.3773 Epoch 2: loss did not improve from 1.22533 63/134 [=============>................] - ETA: 7s - loss: 1.3246 - dice_coefficient: 0.3789 Epoch 2: loss did not improve from 1.22533 64/134 [=============>................] - ETA: 7s - loss: 1.3173 - dice_coefficient: 0.3813 Epoch 2: loss did not improve from 1.22533 65/134 [=============>................] - ETA: 7s - loss: 1.3249 - dice_coefficient: 0.3787 Epoch 2: loss did not improve from 1.22533 66/134 [=============>................] - ETA: 7s - loss: 1.3187 - dice_coefficient: 0.3812 Epoch 2: loss did not improve from 1.22533 67/134 [==============>...............] - ETA: 7s - loss: 1.3152 - dice_coefficient: 0.3827 Epoch 2: loss did not improve from 1.22533 68/134 [==============>...............] - ETA: 6s - loss: 1.3123 - dice_coefficient: 0.3840 Epoch 2: loss did not improve from 1.22533 69/134 [==============>...............] - ETA: 6s - loss: 1.3130 - dice_coefficient: 0.3834 Epoch 2: loss did not improve from 1.22533 70/134 [==============>...............] - ETA: 6s - loss: 1.3101 - dice_coefficient: 0.3843 Epoch 2: loss did not improve from 1.22533 71/134 [==============>...............] - ETA: 6s - loss: 1.3056 - dice_coefficient: 0.3855 Epoch 2: loss did not improve from 1.22533 72/134 [===============>..............] - ETA: 6s - loss: 1.3104 - dice_coefficient: 0.3838 Epoch 2: loss did not improve from 1.22533 73/134 [===============>..............] - ETA: 6s - loss: 1.3085 - dice_coefficient: 0.3844 Epoch 2: loss did not improve from 1.22533 74/134 [===============>..............] - ETA: 6s - loss: 1.3057 - dice_coefficient: 0.3853 Epoch 2: loss did not improve from 1.22533 75/134 [===============>..............] - ETA: 6s - loss: 1.3053 - dice_coefficient: 0.3847 Epoch 2: loss did not improve from 1.22533 76/134 [================>.............] - ETA: 6s - loss: 1.3032 - dice_coefficient: 0.3865 Epoch 2: loss did not improve from 1.22533 77/134 [================>.............] - ETA: 6s - loss: 1.3028 - dice_coefficient: 0.3867 Epoch 2: loss did not improve from 1.22533 78/134 [================>.............] - ETA: 5s - loss: 1.3062 - dice_coefficient: 0.3857 Epoch 2: loss did not improve from 1.22533 79/134 [================>.............] - ETA: 5s - loss: 1.3026 - dice_coefficient: 0.3873 Epoch 2: loss did not improve from 1.22533 80/134 [================>.............] - ETA: 5s - loss: 1.2999 - dice_coefficient: 0.3886 Epoch 2: loss did not improve from 1.22533 81/134 [=================>............] - ETA: 5s - loss: 1.2947 - dice_coefficient: 0.3911 Epoch 2: loss did not improve from 1.22533 82/134 [=================>............] - ETA: 5s - loss: 1.2928 - dice_coefficient: 0.3921 Epoch 2: loss did not improve from 1.22533 83/134 [=================>............] - ETA: 5s - loss: 1.2888 - dice_coefficient: 0.3936 Epoch 2: loss did not improve from 1.22533 84/134 [=================>............] - ETA: 5s - loss: 1.2918 - dice_coefficient: 0.3931 Epoch 2: loss did not improve from 1.22533 85/134 [==================>...........] - ETA: 5s - loss: 1.2921 - dice_coefficient: 0.3924 Epoch 2: loss did not improve from 1.22533 86/134 [==================>...........] - ETA: 5s - loss: 1.2936 - dice_coefficient: 0.3926 Epoch 2: loss did not improve from 1.22533 87/134 [==================>...........] - ETA: 4s - loss: 1.2987 - dice_coefficient: 0.3907 Epoch 2: loss did not improve from 1.22533 88/134 [==================>...........] - ETA: 4s - loss: 1.3033 - dice_coefficient: 0.3887 Epoch 2: loss did not improve from 1.22533 89/134 [==================>...........] - ETA: 4s - loss: 1.3010 - dice_coefficient: 0.3890 Epoch 2: loss did not improve from 1.22533 90/134 [===================>..........] - ETA: 4s - loss: 1.3007 - dice_coefficient: 0.3885 Epoch 2: loss did not improve from 1.22533 91/134 [===================>..........] - ETA: 4s - loss: 1.2977 - dice_coefficient: 0.3893 Epoch 2: loss did not improve from 1.22533 92/134 [===================>..........] - ETA: 4s - loss: 1.2971 - dice_coefficient: 0.3899 Epoch 2: loss did not improve from 1.22533 93/134 [===================>..........] - ETA: 4s - loss: 1.2927 - dice_coefficient: 0.3920 Epoch 2: loss did not improve from 1.22533 94/134 [====================>.........] - ETA: 4s - loss: 1.2913 - dice_coefficient: 0.3922 Epoch 2: loss did not improve from 1.22533 95/134 [====================>.........] - ETA: 4s - loss: 1.2881 - dice_coefficient: 0.3937 Epoch 2: loss did not improve from 1.22533 96/134 [====================>.........] - ETA: 4s - loss: 1.2829 - dice_coefficient: 0.3952 Epoch 2: loss did not improve from 1.22533 97/134 [====================>.........] - ETA: 3s - loss: 1.2802 - dice_coefficient: 0.3961 Epoch 2: loss did not improve from 1.22533 98/134 [====================>.........] - ETA: 3s - loss: 1.2776 - dice_coefficient: 0.3969 Epoch 2: loss did not improve from 1.22533 99/134 [=====================>........] - ETA: 3s - loss: 1.2787 - dice_coefficient: 0.3975 Epoch 2: loss did not improve from 1.22533 100/134 [=====================>........] - ETA: 3s - loss: 1.2833 - dice_coefficient: 0.3955 Epoch 2: loss did not improve from 1.22533 101/134 [=====================>........] - ETA: 3s - loss: 1.2829 - dice_coefficient: 0.3952 Epoch 2: loss did not improve from 1.22533 102/134 [=====================>........] - ETA: 3s - loss: 1.2820 - dice_coefficient: 0.3954 Epoch 2: loss did not improve from 1.22533 103/134 [======================>.......] - ETA: 3s - loss: 1.2817 - dice_coefficient: 0.3955 Epoch 2: loss did not improve from 1.22533 104/134 [======================>.......] - ETA: 3s - loss: 1.2795 - dice_coefficient: 0.3961 Epoch 2: loss did not improve from 1.22533 105/134 [======================>.......] - ETA: 3s - loss: 1.2765 - dice_coefficient: 0.3977 Epoch 2: loss did not improve from 1.22533 106/134 [======================>.......] - ETA: 2s - loss: 1.2731 - dice_coefficient: 0.3990 Epoch 2: loss did not improve from 1.22533 107/134 [======================>.......] - ETA: 2s - loss: 1.2777 - dice_coefficient: 0.3978 Epoch 2: loss did not improve from 1.22533 108/134 [=======================>......] - ETA: 2s - loss: 1.2744 - dice_coefficient: 0.3986 Epoch 2: loss did not improve from 1.22533 109/134 [=======================>......] - ETA: 2s - loss: 1.2752 - dice_coefficient: 0.3981 Epoch 2: loss did not improve from 1.22533 110/134 [=======================>......] - ETA: 2s - loss: 1.2758 - dice_coefficient: 0.3979 Epoch 2: loss did not improve from 1.22533 111/134 [=======================>......] - ETA: 2s - loss: 1.2763 - dice_coefficient: 0.3973 Epoch 2: loss did not improve from 1.22533 112/134 [========================>.....] - ETA: 2s - loss: 1.2731 - dice_coefficient: 0.3984 Epoch 2: loss did not improve from 1.22533 113/134 [========================>.....] - ETA: 2s - loss: 1.2719 - dice_coefficient: 0.3991 Epoch 2: loss did not improve from 1.22533 114/134 [========================>.....] - ETA: 2s - loss: 1.2712 - dice_coefficient: 0.3995 Epoch 2: loss did not improve from 1.22533 115/134 [========================>.....] - ETA: 2s - loss: 1.2696 - dice_coefficient: 0.4002 Epoch 2: loss did not improve from 1.22533 116/134 [========================>.....] - ETA: 1s - loss: 1.2684 - dice_coefficient: 0.4004 Epoch 2: loss did not improve from 1.22533 117/134 [=========================>....] - ETA: 1s - loss: 1.2653 - dice_coefficient: 0.4015 Epoch 2: loss did not improve from 1.22533 118/134 [=========================>....] - ETA: 1s - loss: 1.2661 - dice_coefficient: 0.4007 Epoch 2: loss did not improve from 1.22533 119/134 [=========================>....] - ETA: 1s - loss: 1.2631 - dice_coefficient: 0.4023 Epoch 2: loss did not improve from 1.22533 120/134 [=========================>....] - ETA: 1s - loss: 1.2627 - dice_coefficient: 0.4021 Epoch 2: loss did not improve from 1.22533 121/134 [==========================>...] - ETA: 1s - loss: 1.2621 - dice_coefficient: 0.4019 Epoch 2: loss did not improve from 1.22533 122/134 [==========================>...] - ETA: 1s - loss: 1.2620 - dice_coefficient: 0.4015 Epoch 2: loss did not improve from 1.22533 123/134 [==========================>...] - ETA: 1s - loss: 1.2604 - dice_coefficient: 0.4020 Epoch 2: loss did not improve from 1.22533 124/134 [==========================>...] - ETA: 1s - loss: 1.2564 - dice_coefficient: 0.4035 Epoch 2: loss did not improve from 1.22533 125/134 [==========================>...] - ETA: 0s - loss: 1.2586 - dice_coefficient: 0.4024 Epoch 2: loss did not improve from 1.22533 126/134 [===========================>..] - ETA: 0s - loss: 1.2576 - dice_coefficient: 0.4023 Epoch 2: loss did not improve from 1.22533 127/134 [===========================>..] - ETA: 0s - loss: 1.2579 - dice_coefficient: 0.4022 Epoch 2: loss did not improve from 1.22533 128/134 [===========================>..] - ETA: 0s - loss: 1.2575 - dice_coefficient: 0.4026 Epoch 2: loss did not improve from 1.22533 129/134 [===========================>..] - ETA: 0s - loss: 1.2566 - dice_coefficient: 0.4029 Epoch 2: loss did not improve from 1.22533 130/134 [============================>.] - ETA: 0s - loss: 1.2565 - dice_coefficient: 0.4031 Epoch 2: loss did not improve from 1.22533 131/134 [============================>.] - ETA: 0s - loss: 1.2555 - dice_coefficient: 0.4030 Epoch 2: loss did not improve from 1.22533 132/134 [============================>.] - ETA: 0s - loss: 1.2568 - dice_coefficient: 0.4024 Epoch 2: loss did not improve from 1.22533 133/134 [============================>.] - ETA: 0s - loss: 1.2575 - dice_coefficient: 0.4024 Epoch 2: loss did not improve from 1.22533 134/134 [==============================] - 14s 105ms/step - loss: 1.2566 - dice_coefficient: 0.4038 - lr: 1.0000e-04 Epoch 3/5 Epoch 3: loss improved from 1.22533 to 1.18579, saving model to moblenet_face_detection.h5 1/134 [..............................] - ETA: 31s - loss: 1.1858 - dice_coefficient: 0.3840 Epoch 3: loss improved from 1.18579 to 1.06886, saving model to moblenet_face_detection.h5 2/134 [..............................] - ETA: 30s - loss: 1.0689 - dice_coefficient: 0.4281 Epoch 3: loss improved from 1.06886 to 1.05752, saving model to moblenet_face_detection.h5 3/134 [..............................] - ETA: 32s - loss: 1.0575 - dice_coefficient: 0.4370 Epoch 3: loss improved from 1.05752 to 1.05063, saving model to moblenet_face_detection.h5 4/134 [..............................] - ETA: 31s - loss: 1.0506 - dice_coefficient: 0.4507 Epoch 3: loss improved from 1.05063 to 1.00729, saving model to moblenet_face_detection.h5 5/134 [>.............................] - ETA: 31s - loss: 1.0073 - dice_coefficient: 0.4821 Epoch 3: loss did not improve from 1.00729 6/134 [>.............................] - ETA: 27s - loss: 1.0884 - dice_coefficient: 0.4461 Epoch 3: loss did not improve from 1.00729 7/134 [>.............................] - ETA: 25s - loss: 1.1103 - dice_coefficient: 0.4346 Epoch 3: loss did not improve from 1.00729 8/134 [>.............................] - ETA: 23s - loss: 1.1196 - dice_coefficient: 0.4269 Epoch 3: loss did not improve from 1.00729 9/134 [=>............................] - ETA: 22s - loss: 1.1467 - dice_coefficient: 0.4202 Epoch 3: loss did not improve from 1.00729 10/134 [=>............................] - ETA: 20s - loss: 1.1275 - dice_coefficient: 0.4241 Epoch 3: loss did not improve from 1.00729 11/134 [=>............................] - ETA: 19s - loss: 1.1383 - dice_coefficient: 0.4243 Epoch 3: loss did not improve from 1.00729 12/134 [=>............................] - ETA: 19s - loss: 1.1491 - dice_coefficient: 0.4158 Epoch 3: loss did not improve from 1.00729 13/134 [=>............................] - ETA: 18s - loss: 1.1365 - dice_coefficient: 0.4209 Epoch 3: loss did not improve from 1.00729 14/134 [==>...........................] - ETA: 17s - loss: 1.1532 - dice_coefficient: 0.4171 Epoch 3: loss did not improve from 1.00729 15/134 [==>...........................] - ETA: 17s - loss: 1.1343 - dice_coefficient: 0.4211 Epoch 3: loss did not improve from 1.00729 16/134 [==>...........................] - ETA: 17s - loss: 1.1132 - dice_coefficient: 0.4329 Epoch 3: loss did not improve from 1.00729 17/134 [==>...........................] - ETA: 16s - loss: 1.1026 - dice_coefficient: 0.4354 Epoch 3: loss did not improve from 1.00729 18/134 [===>..........................] - ETA: 16s - loss: 1.1051 - dice_coefficient: 0.4334 Epoch 3: loss did not improve from 1.00729 19/134 [===>..........................] - ETA: 15s - loss: 1.1155 - dice_coefficient: 0.4299 Epoch 3: loss did not improve from 1.00729 20/134 [===>..........................] - ETA: 15s - loss: 1.1046 - dice_coefficient: 0.4354 Epoch 3: loss did not improve from 1.00729 21/134 [===>..........................] - ETA: 15s - loss: 1.1077 - dice_coefficient: 0.4352 Epoch 3: loss did not improve from 1.00729 22/134 [===>..........................] - ETA: 14s - loss: 1.0994 - dice_coefficient: 0.4405 Epoch 3: loss did not improve from 1.00729 23/134 [====>.........................] - ETA: 14s - loss: 1.0953 - dice_coefficient: 0.4427 Epoch 3: loss did not improve from 1.00729 24/134 [====>.........................] - ETA: 14s - loss: 1.0901 - dice_coefficient: 0.4453 Epoch 3: loss did not improve from 1.00729 25/134 [====>.........................] - ETA: 14s - loss: 1.0979 - dice_coefficient: 0.4411 Epoch 3: loss did not improve from 1.00729 26/134 [====>.........................] - ETA: 13s - loss: 1.0965 - dice_coefficient: 0.4420 Epoch 3: loss did not improve from 1.00729 27/134 [=====>........................] - ETA: 13s - loss: 1.0908 - dice_coefficient: 0.4447 Epoch 3: loss did not improve from 1.00729 28/134 [=====>........................] - ETA: 13s - loss: 1.1025 - dice_coefficient: 0.4396 Epoch 3: loss did not improve from 1.00729 29/134 [=====>........................] - ETA: 13s - loss: 1.1126 - dice_coefficient: 0.4348 Epoch 3: loss did not improve from 1.00729 30/134 [=====>........................] - ETA: 13s - loss: 1.1106 - dice_coefficient: 0.4387 Epoch 3: loss did not improve from 1.00729 31/134 [=====>........................] - ETA: 12s - loss: 1.1043 - dice_coefficient: 0.4420 Epoch 3: loss did not improve from 1.00729 32/134 [======>.......................] - ETA: 12s - loss: 1.0992 - dice_coefficient: 0.4438 Epoch 3: loss did not improve from 1.00729 33/134 [======>.......................] - ETA: 12s - loss: 1.1049 - dice_coefficient: 0.4427 Epoch 3: loss did not improve from 1.00729 34/134 [======>.......................] - ETA: 12s - loss: 1.1032 - dice_coefficient: 0.4423 Epoch 3: loss did not improve from 1.00729 35/134 [======>.......................] - ETA: 12s - loss: 1.1016 - dice_coefficient: 0.4440 Epoch 3: loss did not improve from 1.00729 36/134 [=======>......................] - ETA: 11s - loss: 1.1006 - dice_coefficient: 0.4460 Epoch 3: loss did not improve from 1.00729 37/134 [=======>......................] - ETA: 11s - loss: 1.0950 - dice_coefficient: 0.4466 Epoch 3: loss did not improve from 1.00729 38/134 [=======>......................] - ETA: 11s - loss: 1.0950 - dice_coefficient: 0.4459 Epoch 3: loss did not improve from 1.00729 39/134 [=======>......................] - ETA: 11s - loss: 1.0879 - dice_coefficient: 0.4480 Epoch 3: loss did not improve from 1.00729 40/134 [=======>......................] - ETA: 11s - loss: 1.0831 - dice_coefficient: 0.4500 Epoch 3: loss did not improve from 1.00729 41/134 [========>.....................] - ETA: 11s - loss: 1.0868 - dice_coefficient: 0.4502 Epoch 3: loss did not improve from 1.00729 42/134 [========>.....................] - ETA: 11s - loss: 1.0808 - dice_coefficient: 0.4520 Epoch 3: loss did not improve from 1.00729 43/134 [========>.....................] - ETA: 10s - loss: 1.0813 - dice_coefficient: 0.4526 Epoch 3: loss did not improve from 1.00729 44/134 [========>.....................] - ETA: 10s - loss: 1.0789 - dice_coefficient: 0.4534 Epoch 3: loss did not improve from 1.00729 45/134 [=========>....................] - ETA: 10s - loss: 1.0767 - dice_coefficient: 0.4546 Epoch 3: loss did not improve from 1.00729 46/134 [=========>....................] - ETA: 10s - loss: 1.0747 - dice_coefficient: 0.4554 Epoch 3: loss did not improve from 1.00729 47/134 [=========>....................] - ETA: 10s - loss: 1.0664 - dice_coefficient: 0.4598 Epoch 3: loss did not improve from 1.00729 48/134 [=========>....................] - ETA: 10s - loss: 1.0777 - dice_coefficient: 0.4553 Epoch 3: loss did not improve from 1.00729 49/134 [=========>....................] - ETA: 10s - loss: 1.0788 - dice_coefficient: 0.4559 Epoch 3: loss did not improve from 1.00729 50/134 [==========>...................] - ETA: 9s - loss: 1.0738 - dice_coefficient: 0.4591 Epoch 3: loss did not improve from 1.00729 51/134 [==========>...................] - ETA: 9s - loss: 1.0677 - dice_coefficient: 0.4605 Epoch 3: loss did not improve from 1.00729 52/134 [==========>...................] - ETA: 9s - loss: 1.0686 - dice_coefficient: 0.4616 Epoch 3: loss did not improve from 1.00729 53/134 [==========>...................] - ETA: 9s - loss: 1.0779 - dice_coefficient: 0.4596 Epoch 3: loss did not improve from 1.00729 54/134 [===========>..................] - ETA: 9s - loss: 1.0744 - dice_coefficient: 0.4615 Epoch 3: loss did not improve from 1.00729 55/134 [===========>..................] - ETA: 9s - loss: 1.0721 - dice_coefficient: 0.4621 Epoch 3: loss did not improve from 1.00729 56/134 [===========>..................] - ETA: 9s - loss: 1.0711 - dice_coefficient: 0.4617 Epoch 3: loss did not improve from 1.00729 57/134 [===========>..................] - ETA: 8s - loss: 1.0730 - dice_coefficient: 0.4609 Epoch 3: loss did not improve from 1.00729 58/134 [===========>..................] - ETA: 8s - loss: 1.0718 - dice_coefficient: 0.4619 Epoch 3: loss did not improve from 1.00729 59/134 [============>.................] - ETA: 8s - loss: 1.0667 - dice_coefficient: 0.4637 Epoch 3: loss did not improve from 1.00729 60/134 [============>.................] - ETA: 8s - loss: 1.0691 - dice_coefficient: 0.4621 Epoch 3: loss did not improve from 1.00729 61/134 [============>.................] - ETA: 8s - loss: 1.0670 - dice_coefficient: 0.4632 Epoch 3: loss did not improve from 1.00729 62/134 [============>.................] - ETA: 8s - loss: 1.0663 - dice_coefficient: 0.4627 Epoch 3: loss did not improve from 1.00729 63/134 [=============>................] - ETA: 8s - loss: 1.0616 - dice_coefficient: 0.4653 Epoch 3: loss did not improve from 1.00729 64/134 [=============>................] - ETA: 8s - loss: 1.0652 - dice_coefficient: 0.4630 Epoch 3: loss did not improve from 1.00729 65/134 [=============>................] - ETA: 7s - loss: 1.0633 - dice_coefficient: 0.4641 Epoch 3: loss did not improve from 1.00729 66/134 [=============>................] - ETA: 7s - loss: 1.0618 - dice_coefficient: 0.4638 Epoch 3: loss did not improve from 1.00729 67/134 [==============>...............] - ETA: 7s - loss: 1.0610 - dice_coefficient: 0.4638 Epoch 3: loss did not improve from 1.00729 68/134 [==============>...............] - ETA: 7s - loss: 1.0607 - dice_coefficient: 0.4634 Epoch 3: loss did not improve from 1.00729 69/134 [==============>...............] - ETA: 7s - loss: 1.0563 - dice_coefficient: 0.4655 Epoch 3: loss did not improve from 1.00729 70/134 [==============>...............] - ETA: 7s - loss: 1.0653 - dice_coefficient: 0.4621 Epoch 3: loss did not improve from 1.00729 71/134 [==============>...............] - ETA: 7s - loss: 1.0734 - dice_coefficient: 0.4593 Epoch 3: loss did not improve from 1.00729 72/134 [===============>..............] - ETA: 7s - loss: 1.0713 - dice_coefficient: 0.4597 Epoch 3: loss did not improve from 1.00729 73/134 [===============>..............] - ETA: 6s - loss: 1.0740 - dice_coefficient: 0.4590 Epoch 3: loss did not improve from 1.00729 74/134 [===============>..............] - ETA: 6s - loss: 1.0733 - dice_coefficient: 0.4602 Epoch 3: loss did not improve from 1.00729 75/134 [===============>..............] - ETA: 6s - loss: 1.0738 - dice_coefficient: 0.4598 Epoch 3: loss did not improve from 1.00729 76/134 [================>.............] - ETA: 6s - loss: 1.0734 - dice_coefficient: 0.4598 Epoch 3: loss did not improve from 1.00729 77/134 [================>.............] - ETA: 6s - loss: 1.0748 - dice_coefficient: 0.4601 Epoch 3: loss did not improve from 1.00729 78/134 [================>.............] - ETA: 6s - loss: 1.0739 - dice_coefficient: 0.4617 Epoch 3: loss did not improve from 1.00729 79/134 [================>.............] - ETA: 6s - loss: 1.0751 - dice_coefficient: 0.4613 Epoch 3: loss did not improve from 1.00729 80/134 [================>.............] - ETA: 6s - loss: 1.0748 - dice_coefficient: 0.4611 Epoch 3: loss did not improve from 1.00729 81/134 [=================>............] - ETA: 6s - loss: 1.0727 - dice_coefficient: 0.4619 Epoch 3: loss did not improve from 1.00729 82/134 [=================>............] - ETA: 5s - loss: 1.0699 - dice_coefficient: 0.4631 Epoch 3: loss did not improve from 1.00729 83/134 [=================>............] - ETA: 5s - loss: 1.0710 - dice_coefficient: 0.4627 Epoch 3: loss did not improve from 1.00729 84/134 [=================>............] - ETA: 5s - loss: 1.0766 - dice_coefficient: 0.4621 Epoch 3: loss did not improve from 1.00729 85/134 [==================>...........] - ETA: 5s - loss: 1.0775 - dice_coefficient: 0.4614 Epoch 3: loss did not improve from 1.00729 86/134 [==================>...........] - ETA: 5s - loss: 1.0774 - dice_coefficient: 0.4609 Epoch 3: loss did not improve from 1.00729 87/134 [==================>...........] - ETA: 5s - loss: 1.0793 - dice_coefficient: 0.4601 Epoch 3: loss did not improve from 1.00729 88/134 [==================>...........] - ETA: 5s - loss: 1.0805 - dice_coefficient: 0.4592 Epoch 3: loss did not improve from 1.00729 89/134 [==================>...........] - ETA: 5s - loss: 1.0819 - dice_coefficient: 0.4584 Epoch 3: loss did not improve from 1.00729 90/134 [===================>..........] - ETA: 4s - loss: 1.0794 - dice_coefficient: 0.4589 Epoch 3: loss did not improve from 1.00729 91/134 [===================>..........] - ETA: 4s - loss: 1.0799 - dice_coefficient: 0.4591 Epoch 3: loss did not improve from 1.00729 92/134 [===================>..........] - ETA: 4s - loss: 1.0757 - dice_coefficient: 0.4608 Epoch 3: loss did not improve from 1.00729 93/134 [===================>..........] - ETA: 4s - loss: 1.0766 - dice_coefficient: 0.4603 Epoch 3: loss did not improve from 1.00729 94/134 [====================>.........] - ETA: 4s - loss: 1.0737 - dice_coefficient: 0.4615 Epoch 3: loss did not improve from 1.00729 95/134 [====================>.........] - ETA: 4s - loss: 1.0796 - dice_coefficient: 0.4594 Epoch 3: loss did not improve from 1.00729 96/134 [====================>.........] - ETA: 4s - loss: 1.0801 - dice_coefficient: 0.4592 Epoch 3: loss did not improve from 1.00729 97/134 [====================>.........] - ETA: 4s - loss: 1.0790 - dice_coefficient: 0.4592 Epoch 3: loss did not improve from 1.00729 98/134 [====================>.........] - ETA: 4s - loss: 1.0796 - dice_coefficient: 0.4585 Epoch 3: loss did not improve from 1.00729 99/134 [=====================>........] - ETA: 3s - loss: 1.0768 - dice_coefficient: 0.4594 Epoch 3: loss did not improve from 1.00729 100/134 [=====================>........] - ETA: 3s - loss: 1.0746 - dice_coefficient: 0.4599 Epoch 3: loss did not improve from 1.00729 101/134 [=====================>........] - ETA: 3s - loss: 1.0731 - dice_coefficient: 0.4605 Epoch 3: loss did not improve from 1.00729 102/134 [=====================>........] - ETA: 3s - loss: 1.0723 - dice_coefficient: 0.4610 Epoch 3: loss did not improve from 1.00729 103/134 [======================>.......] - ETA: 3s - loss: 1.0730 - dice_coefficient: 0.4604 Epoch 3: loss did not improve from 1.00729 104/134 [======================>.......] - ETA: 3s - loss: 1.0726 - dice_coefficient: 0.4604 Epoch 3: loss did not improve from 1.00729 105/134 [======================>.......] - ETA: 3s - loss: 1.0700 - dice_coefficient: 0.4612 Epoch 3: loss did not improve from 1.00729 106/134 [======================>.......] - ETA: 3s - loss: 1.0689 - dice_coefficient: 0.4615 Epoch 3: loss did not improve from 1.00729 107/134 [======================>.......] - ETA: 3s - loss: 1.0734 - dice_coefficient: 0.4598 Epoch 3: loss did not improve from 1.00729 108/134 [=======================>......] - ETA: 2s - loss: 1.0725 - dice_coefficient: 0.4600 Epoch 3: loss did not improve from 1.00729 109/134 [=======================>......] - ETA: 2s - loss: 1.0711 - dice_coefficient: 0.4606 Epoch 3: loss did not improve from 1.00729 110/134 [=======================>......] - ETA: 2s - loss: 1.0703 - dice_coefficient: 0.4613 Epoch 3: loss did not improve from 1.00729 111/134 [=======================>......] - ETA: 2s - loss: 1.0725 - dice_coefficient: 0.4610 Epoch 3: loss did not improve from 1.00729 112/134 [========================>.....] - ETA: 2s - loss: 1.0712 - dice_coefficient: 0.4612 Epoch 3: loss did not improve from 1.00729 113/134 [========================>.....] - ETA: 2s - loss: 1.0738 - dice_coefficient: 0.4610 Epoch 3: loss did not improve from 1.00729 114/134 [========================>.....] - ETA: 2s - loss: 1.0729 - dice_coefficient: 0.4614 Epoch 3: loss did not improve from 1.00729 115/134 [========================>.....] - ETA: 2s - loss: 1.0732 - dice_coefficient: 0.4611 Epoch 3: loss did not improve from 1.00729 116/134 [========================>.....] - ETA: 2s - loss: 1.0710 - dice_coefficient: 0.4621 Epoch 3: loss did not improve from 1.00729 117/134 [=========================>....] - ETA: 1s - loss: 1.0731 - dice_coefficient: 0.4612 Epoch 3: loss did not improve from 1.00729 118/134 [=========================>....] - ETA: 1s - loss: 1.0718 - dice_coefficient: 0.4621 Epoch 3: loss did not improve from 1.00729 119/134 [=========================>....] - ETA: 1s - loss: 1.0709 - dice_coefficient: 0.4623 Epoch 3: loss did not improve from 1.00729 120/134 [=========================>....] - ETA: 1s - loss: 1.0681 - dice_coefficient: 0.4635 Epoch 3: loss did not improve from 1.00729 121/134 [==========================>...] - ETA: 1s - loss: 1.0657 - dice_coefficient: 0.4647 Epoch 3: loss did not improve from 1.00729 122/134 [==========================>...] - ETA: 1s - loss: 1.0666 - dice_coefficient: 0.4638 Epoch 3: loss did not improve from 1.00729 123/134 [==========================>...] - ETA: 1s - loss: 1.0658 - dice_coefficient: 0.4644 Epoch 3: loss did not improve from 1.00729 124/134 [==========================>...] - ETA: 1s - loss: 1.0643 - dice_coefficient: 0.4656 Epoch 3: loss did not improve from 1.00729 125/134 [==========================>...] - ETA: 1s - loss: 1.0641 - dice_coefficient: 0.4653 Epoch 3: loss did not improve from 1.00729 126/134 [===========================>..] - ETA: 0s - loss: 1.0637 - dice_coefficient: 0.4657 Epoch 3: loss did not improve from 1.00729 127/134 [===========================>..] - ETA: 0s - loss: 1.0609 - dice_coefficient: 0.4671 Epoch 3: loss did not improve from 1.00729 128/134 [===========================>..] - ETA: 0s - loss: 1.0615 - dice_coefficient: 0.4665 Epoch 3: loss did not improve from 1.00729 129/134 [===========================>..] - ETA: 0s - loss: 1.0609 - dice_coefficient: 0.4664 Epoch 3: loss did not improve from 1.00729 130/134 [============================>.] - ETA: 0s - loss: 1.0589 - dice_coefficient: 0.4668 Epoch 3: loss did not improve from 1.00729 131/134 [============================>.] - ETA: 0s - loss: 1.0588 - dice_coefficient: 0.4666 Epoch 3: loss did not improve from 1.00729 132/134 [============================>.] - ETA: 0s - loss: 1.0575 - dice_coefficient: 0.4674 Epoch 3: loss did not improve from 1.00729 133/134 [============================>.] - ETA: 0s - loss: 1.0566 - dice_coefficient: 0.4679 Epoch 3: loss did not improve from 1.00729 134/134 [==============================] - 15s 111ms/step - loss: 1.0559 - dice_coefficient: 0.4681 - lr: 1.0000e-04 Epoch 4/5 Epoch 4: loss improved from 1.00729 to 0.84602, saving model to moblenet_face_detection.h5 1/134 [..............................] - ETA: 31s - loss: 0.8460 - dice_coefficient: 0.5581 Epoch 4: loss did not improve from 0.84602 2/134 [..............................] - ETA: 15s - loss: 0.8681 - dice_coefficient: 0.5193 Epoch 4: loss did not improve from 0.84602 3/134 [..............................] - ETA: 14s - loss: 0.9550 - dice_coefficient: 0.4921 Epoch 4: loss did not improve from 0.84602 4/134 [..............................] - ETA: 14s - loss: 0.9508 - dice_coefficient: 0.5067 Epoch 4: loss did not improve from 0.84602 5/134 [>.............................] - ETA: 14s - loss: 0.8718 - dice_coefficient: 0.5378 Epoch 4: loss did not improve from 0.84602 6/134 [>.............................] - ETA: 13s - loss: 0.9373 - dice_coefficient: 0.5044 Epoch 4: loss did not improve from 0.84602 7/134 [>.............................] - ETA: 13s - loss: 0.9250 - dice_coefficient: 0.5136 Epoch 4: loss did not improve from 0.84602 8/134 [>.............................] - ETA: 13s - loss: 0.9118 - dice_coefficient: 0.5139 Epoch 4: loss did not improve from 0.84602 9/134 [=>............................] - ETA: 13s - loss: 0.8833 - dice_coefficient: 0.5323 Epoch 4: loss did not improve from 0.84602 10/134 [=>............................] - ETA: 13s - loss: 0.8841 - dice_coefficient: 0.5320 Epoch 4: loss did not improve from 0.84602 11/134 [=>............................] - ETA: 13s - loss: 0.8981 - dice_coefficient: 0.5274 Epoch 4: loss did not improve from 0.84602 12/134 [=>............................] - ETA: 13s - loss: 0.9226 - dice_coefficient: 0.5148 Epoch 4: loss did not improve from 0.84602 13/134 [=>............................] - ETA: 13s - loss: 0.9350 - dice_coefficient: 0.5150 Epoch 4: loss did not improve from 0.84602 14/134 [==>...........................] - ETA: 12s - loss: 0.9260 - dice_coefficient: 0.5187 Epoch 4: loss did not improve from 0.84602 15/134 [==>...........................] - ETA: 12s - loss: 0.9193 - dice_coefficient: 0.5232 Epoch 4: loss did not improve from 0.84602 16/134 [==>...........................] - ETA: 12s - loss: 0.9465 - dice_coefficient: 0.5096 Epoch 4: loss did not improve from 0.84602 17/134 [==>...........................] - ETA: 12s - loss: 0.9401 - dice_coefficient: 0.5107 Epoch 4: loss did not improve from 0.84602 18/134 [===>..........................] - ETA: 12s - loss: 0.9480 - dice_coefficient: 0.5095 Epoch 4: loss did not improve from 0.84602 19/134 [===>..........................] - ETA: 12s - loss: 0.9477 - dice_coefficient: 0.5084 Epoch 4: loss did not improve from 0.84602 20/134 [===>..........................] - ETA: 12s - loss: 0.9439 - dice_coefficient: 0.5114 Epoch 4: loss did not improve from 0.84602 21/134 [===>..........................] - ETA: 12s - loss: 0.9403 - dice_coefficient: 0.5134 Epoch 4: loss did not improve from 0.84602 22/134 [===>..........................] - ETA: 12s - loss: 0.9529 - dice_coefficient: 0.5128 Epoch 4: loss did not improve from 0.84602 23/134 [====>.........................] - ETA: 11s - loss: 0.9600 - dice_coefficient: 0.5076 Epoch 4: loss did not improve from 0.84602 24/134 [====>.........................] - ETA: 11s - loss: 0.9553 - dice_coefficient: 0.5080 Epoch 4: loss did not improve from 0.84602 25/134 [====>.........................] - ETA: 11s - loss: 0.9624 - dice_coefficient: 0.5054 Epoch 4: loss did not improve from 0.84602 26/134 [====>.........................] - ETA: 11s - loss: 0.9587 - dice_coefficient: 0.5091 Epoch 4: loss did not improve from 0.84602 27/134 [=====>........................] - ETA: 11s - loss: 0.9620 - dice_coefficient: 0.5059 Epoch 4: loss did not improve from 0.84602 28/134 [=====>........................] - ETA: 11s - loss: 0.9588 - dice_coefficient: 0.5072 Epoch 4: loss did not improve from 0.84602 29/134 [=====>........................] - ETA: 11s - loss: 0.9668 - dice_coefficient: 0.5042 Epoch 4: loss did not improve from 0.84602 30/134 [=====>........................] - ETA: 11s - loss: 0.9713 - dice_coefficient: 0.5001 Epoch 4: loss did not improve from 0.84602 31/134 [=====>........................] - ETA: 11s - loss: 0.9611 - dice_coefficient: 0.5045 Epoch 4: loss did not improve from 0.84602 32/134 [======>.......................] - ETA: 10s - loss: 0.9617 - dice_coefficient: 0.5029 Epoch 4: loss did not improve from 0.84602 33/134 [======>.......................] - ETA: 10s - loss: 0.9634 - dice_coefficient: 0.5019 Epoch 4: loss did not improve from 0.84602 34/134 [======>.......................] - ETA: 10s - loss: 0.9662 - dice_coefficient: 0.4997 Epoch 4: loss did not improve from 0.84602 35/134 [======>.......................] - ETA: 10s - loss: 0.9671 - dice_coefficient: 0.4992 Epoch 4: loss did not improve from 0.84602 36/134 [=======>......................] - ETA: 10s - loss: 0.9702 - dice_coefficient: 0.4996 Epoch 4: loss did not improve from 0.84602 37/134 [=======>......................] - ETA: 10s - loss: 0.9637 - dice_coefficient: 0.5028 Epoch 4: loss did not improve from 0.84602 38/134 [=======>......................] - ETA: 10s - loss: 0.9804 - dice_coefficient: 0.4963 Epoch 4: loss did not improve from 0.84602 39/134 [=======>......................] - ETA: 10s - loss: 0.9806 - dice_coefficient: 0.4981 Epoch 4: loss did not improve from 0.84602 40/134 [=======>......................] - ETA: 10s - loss: 0.9798 - dice_coefficient: 0.4976 Epoch 4: loss did not improve from 0.84602 41/134 [========>.....................] - ETA: 10s - loss: 1.0054 - dice_coefficient: 0.4896 Epoch 4: loss did not improve from 0.84602 42/134 [========>.....................] - ETA: 9s - loss: 1.0025 - dice_coefficient: 0.4914 Epoch 4: loss did not improve from 0.84602 43/134 [========>.....................] - ETA: 9s - loss: 1.0206 - dice_coefficient: 0.4847 Epoch 4: loss did not improve from 0.84602 44/134 [========>.....................] - ETA: 9s - loss: 1.0212 - dice_coefficient: 0.4832 Epoch 4: loss did not improve from 0.84602 45/134 [=========>....................] - ETA: 9s - loss: 1.0225 - dice_coefficient: 0.4834 Epoch 4: loss did not improve from 0.84602 46/134 [=========>....................] - ETA: 9s - loss: 1.0281 - dice_coefficient: 0.4804 Epoch 4: loss did not improve from 0.84602 47/134 [=========>....................] - ETA: 9s - loss: 1.0289 - dice_coefficient: 0.4786 Epoch 4: loss did not improve from 0.84602 48/134 [=========>....................] - ETA: 9s - loss: 1.0292 - dice_coefficient: 0.4795 Epoch 4: loss did not improve from 0.84602 49/134 [=========>....................] - ETA: 9s - loss: 1.0249 - dice_coefficient: 0.4821 Epoch 4: loss did not improve from 0.84602 50/134 [==========>...................] - ETA: 9s - loss: 1.0234 - dice_coefficient: 0.4823 Epoch 4: loss did not improve from 0.84602 51/134 [==========>...................] - ETA: 8s - loss: 1.0209 - dice_coefficient: 0.4857 Epoch 4: loss did not improve from 0.84602 52/134 [==========>...................] - ETA: 8s - loss: 1.0181 - dice_coefficient: 0.4869 Epoch 4: loss did not improve from 0.84602 53/134 [==========>...................] - ETA: 8s - loss: 1.0132 - dice_coefficient: 0.4895 Epoch 4: loss did not improve from 0.84602 54/134 [===========>..................] - ETA: 8s - loss: 1.0119 - dice_coefficient: 0.4894 Epoch 4: loss did not improve from 0.84602 55/134 [===========>..................] - ETA: 8s - loss: 1.0130 - dice_coefficient: 0.4882 Epoch 4: loss did not improve from 0.84602 56/134 [===========>..................] - ETA: 8s - loss: 1.0101 - dice_coefficient: 0.4896 Epoch 4: loss did not improve from 0.84602 57/134 [===========>..................] - ETA: 8s - loss: 1.0053 - dice_coefficient: 0.4915 Epoch 4: loss did not improve from 0.84602 58/134 [===========>..................] - ETA: 8s - loss: 1.0098 - dice_coefficient: 0.4898 Epoch 4: loss did not improve from 0.84602 59/134 [============>.................] - ETA: 8s - loss: 1.0076 - dice_coefficient: 0.4898 Epoch 4: loss did not improve from 0.84602 60/134 [============>.................] - ETA: 7s - loss: 1.0049 - dice_coefficient: 0.4909 Epoch 4: loss did not improve from 0.84602 61/134 [============>.................] - ETA: 7s - loss: 1.0014 - dice_coefficient: 0.4919 Epoch 4: loss did not improve from 0.84602 62/134 [============>.................] - ETA: 7s - loss: 0.9993 - dice_coefficient: 0.4933 Epoch 4: loss did not improve from 0.84602 63/134 [=============>................] - ETA: 7s - loss: 1.0016 - dice_coefficient: 0.4931 Epoch 4: loss did not improve from 0.84602 64/134 [=============>................] - ETA: 7s - loss: 1.0038 - dice_coefficient: 0.4934 Epoch 4: loss did not improve from 0.84602 65/134 [=============>................] - ETA: 7s - loss: 1.0024 - dice_coefficient: 0.4931 Epoch 4: loss did not improve from 0.84602 66/134 [=============>................] - ETA: 7s - loss: 0.9999 - dice_coefficient: 0.4944 Epoch 4: loss did not improve from 0.84602 67/134 [==============>...............] - ETA: 7s - loss: 0.9952 - dice_coefficient: 0.4966 Epoch 4: loss did not improve from 0.84602 68/134 [==============>...............] - ETA: 7s - loss: 0.9953 - dice_coefficient: 0.4972 Epoch 4: loss did not improve from 0.84602 69/134 [==============>...............] - ETA: 7s - loss: 0.9920 - dice_coefficient: 0.4982 Epoch 4: loss did not improve from 0.84602 70/134 [==============>...............] - ETA: 7s - loss: 0.9925 - dice_coefficient: 0.4971 Epoch 4: loss did not improve from 0.84602 71/134 [==============>...............] - ETA: 6s - loss: 0.9898 - dice_coefficient: 0.4985 Epoch 4: loss did not improve from 0.84602 72/134 [===============>..............] - ETA: 6s - loss: 0.9878 - dice_coefficient: 0.4997 Epoch 4: loss did not improve from 0.84602 73/134 [===============>..............] - ETA: 6s - loss: 0.9882 - dice_coefficient: 0.4992 Epoch 4: loss did not improve from 0.84602 74/134 [===============>..............] - ETA: 6s - loss: 1.0042 - dice_coefficient: 0.4951 Epoch 4: loss did not improve from 0.84602 75/134 [===============>..............] - ETA: 6s - loss: 1.0038 - dice_coefficient: 0.4948 Epoch 4: loss did not improve from 0.84602 76/134 [================>.............] - ETA: 6s - loss: 1.0006 - dice_coefficient: 0.4968 Epoch 4: loss did not improve from 0.84602 77/134 [================>.............] - ETA: 6s - loss: 0.9987 - dice_coefficient: 0.4977 Epoch 4: loss did not improve from 0.84602 78/134 [================>.............] - ETA: 6s - loss: 0.9966 - dice_coefficient: 0.4991 Epoch 4: loss did not improve from 0.84602 79/134 [================>.............] - ETA: 6s - loss: 0.9962 - dice_coefficient: 0.4987 Epoch 4: loss did not improve from 0.84602 80/134 [================>.............] - ETA: 5s - loss: 0.9926 - dice_coefficient: 0.4999 Epoch 4: loss did not improve from 0.84602 81/134 [=================>............] - ETA: 5s - loss: 0.9948 - dice_coefficient: 0.4997 Epoch 4: loss did not improve from 0.84602 82/134 [=================>............] - ETA: 5s - loss: 0.9930 - dice_coefficient: 0.5003 Epoch 4: loss did not improve from 0.84602 83/134 [=================>............] - ETA: 5s - loss: 0.9949 - dice_coefficient: 0.4990 Epoch 4: loss did not improve from 0.84602 84/134 [=================>............] - ETA: 5s - loss: 0.9924 - dice_coefficient: 0.5002 Epoch 4: loss did not improve from 0.84602 85/134 [==================>...........] - ETA: 5s - loss: 0.9899 - dice_coefficient: 0.5008 Epoch 4: loss did not improve from 0.84602 86/134 [==================>...........] - ETA: 5s - loss: 0.9866 - dice_coefficient: 0.5029 Epoch 4: loss did not improve from 0.84602 87/134 [==================>...........] - ETA: 5s - loss: 0.9866 - dice_coefficient: 0.5020 Epoch 4: loss did not improve from 0.84602 88/134 [==================>...........] - ETA: 5s - loss: 0.9874 - dice_coefficient: 0.5009 Epoch 4: loss did not improve from 0.84602 89/134 [==================>...........] - ETA: 5s - loss: 0.9914 - dice_coefficient: 0.4990 Epoch 4: loss did not improve from 0.84602 90/134 [===================>..........] - ETA: 4s - loss: 0.9917 - dice_coefficient: 0.4991 Epoch 4: loss did not improve from 0.84602 91/134 [===================>..........] - ETA: 4s - loss: 0.9891 - dice_coefficient: 0.5002 Epoch 4: loss did not improve from 0.84602 92/134 [===================>..........] - ETA: 4s - loss: 0.9934 - dice_coefficient: 0.4982 Epoch 4: loss did not improve from 0.84602 93/134 [===================>..........] - ETA: 4s - loss: 0.9948 - dice_coefficient: 0.4976 Epoch 4: loss did not improve from 0.84602 94/134 [====================>.........] - ETA: 4s - loss: 0.9940 - dice_coefficient: 0.4979 Epoch 4: loss did not improve from 0.84602 95/134 [====================>.........] - ETA: 4s - loss: 0.9940 - dice_coefficient: 0.4980 Epoch 4: loss did not improve from 0.84602 96/134 [====================>.........] - ETA: 4s - loss: 0.9925 - dice_coefficient: 0.4985 Epoch 4: loss did not improve from 0.84602 97/134 [====================>.........] - ETA: 4s - loss: 0.9892 - dice_coefficient: 0.4999 Epoch 4: loss did not improve from 0.84602 98/134 [====================>.........] - ETA: 3s - loss: 0.9870 - dice_coefficient: 0.5004 Epoch 4: loss did not improve from 0.84602 99/134 [=====================>........] - ETA: 3s - loss: 0.9850 - dice_coefficient: 0.5018 Epoch 4: loss did not improve from 0.84602 100/134 [=====================>........] - ETA: 3s - loss: 0.9840 - dice_coefficient: 0.5016 Epoch 4: loss did not improve from 0.84602 101/134 [=====================>........] - ETA: 3s - loss: 0.9862 - dice_coefficient: 0.4999 Epoch 4: loss did not improve from 0.84602 102/134 [=====================>........] - ETA: 3s - loss: 0.9861 - dice_coefficient: 0.5002 Epoch 4: loss did not improve from 0.84602 103/134 [======================>.......] - ETA: 3s - loss: 0.9853 - dice_coefficient: 0.5005 Epoch 4: loss did not improve from 0.84602 104/134 [======================>.......] - ETA: 3s - loss: 0.9831 - dice_coefficient: 0.5008 Epoch 4: loss did not improve from 0.84602 105/134 [======================>.......] - ETA: 3s - loss: 0.9832 - dice_coefficient: 0.5004 Epoch 4: loss did not improve from 0.84602 106/134 [======================>.......] - ETA: 3s - loss: 0.9855 - dice_coefficient: 0.4994 Epoch 4: loss did not improve from 0.84602 107/134 [======================>.......] - ETA: 2s - loss: 0.9869 - dice_coefficient: 0.4985 Epoch 4: loss did not improve from 0.84602 108/134 [=======================>......] - ETA: 2s - loss: 0.9862 - dice_coefficient: 0.4982 Epoch 4: loss did not improve from 0.84602 109/134 [=======================>......] - ETA: 2s - loss: 0.9861 - dice_coefficient: 0.4975 Epoch 4: loss did not improve from 0.84602 110/134 [=======================>......] - ETA: 2s - loss: 0.9849 - dice_coefficient: 0.4976 Epoch 4: loss did not improve from 0.84602 111/134 [=======================>......] - ETA: 2s - loss: 0.9856 - dice_coefficient: 0.4978 Epoch 4: loss did not improve from 0.84602 112/134 [========================>.....] - ETA: 2s - loss: 0.9828 - dice_coefficient: 0.4990 Epoch 4: loss did not improve from 0.84602 113/134 [========================>.....] - ETA: 2s - loss: 0.9807 - dice_coefficient: 0.4994 Epoch 4: loss did not improve from 0.84602 114/134 [========================>.....] - ETA: 2s - loss: 0.9800 - dice_coefficient: 0.4996 Epoch 4: loss did not improve from 0.84602 115/134 [========================>.....] - ETA: 2s - loss: 0.9795 - dice_coefficient: 0.4999 Epoch 4: loss did not improve from 0.84602 116/134 [========================>.....] - ETA: 1s - loss: 0.9788 - dice_coefficient: 0.5004 Epoch 4: loss did not improve from 0.84602 117/134 [=========================>....] - ETA: 1s - loss: 0.9769 - dice_coefficient: 0.5007 Epoch 4: loss did not improve from 0.84602 118/134 [=========================>....] - ETA: 1s - loss: 0.9756 - dice_coefficient: 0.5008 Epoch 4: loss did not improve from 0.84602 119/134 [=========================>....] - ETA: 1s - loss: 0.9743 - dice_coefficient: 0.5014 Epoch 4: loss did not improve from 0.84602 120/134 [=========================>....] - ETA: 1s - loss: 0.9722 - dice_coefficient: 0.5022 Epoch 4: loss did not improve from 0.84602 121/134 [==========================>...] - ETA: 1s - loss: 0.9755 - dice_coefficient: 0.5014 Epoch 4: loss did not improve from 0.84602 122/134 [==========================>...] - ETA: 1s - loss: 0.9769 - dice_coefficient: 0.5009 Epoch 4: loss did not improve from 0.84602 123/134 [==========================>...] - ETA: 1s - loss: 0.9784 - dice_coefficient: 0.4999 Epoch 4: loss did not improve from 0.84602 124/134 [==========================>...] - ETA: 1s - loss: 0.9770 - dice_coefficient: 0.5004 Epoch 4: loss did not improve from 0.84602 125/134 [==========================>...] - ETA: 0s - loss: 0.9807 - dice_coefficient: 0.4990 Epoch 4: loss did not improve from 0.84602 126/134 [===========================>..] - ETA: 0s - loss: 0.9786 - dice_coefficient: 0.4997 Epoch 4: loss did not improve from 0.84602 127/134 [===========================>..] - ETA: 0s - loss: 0.9783 - dice_coefficient: 0.5000 Epoch 4: loss did not improve from 0.84602 128/134 [===========================>..] - ETA: 0s - loss: 0.9767 - dice_coefficient: 0.5009 Epoch 4: loss did not improve from 0.84602 129/134 [===========================>..] - ETA: 0s - loss: 0.9778 - dice_coefficient: 0.5004 Epoch 4: loss did not improve from 0.84602 130/134 [============================>.] - ETA: 0s - loss: 0.9769 - dice_coefficient: 0.5005 Epoch 4: loss did not improve from 0.84602 131/134 [============================>.] - ETA: 0s - loss: 0.9771 - dice_coefficient: 0.5007 Epoch 4: loss did not improve from 0.84602 132/134 [============================>.] - ETA: 0s - loss: 0.9782 - dice_coefficient: 0.5002 Epoch 4: loss did not improve from 0.84602 133/134 [============================>.] - ETA: 0s - loss: 0.9786 - dice_coefficient: 0.5000 Epoch 4: loss did not improve from 0.84602 134/134 [==============================] - 15s 110ms/step - loss: 0.9787 - dice_coefficient: 0.4992 - lr: 1.0000e-04 Epoch 5/5 Epoch 5: loss improved from 0.84602 to 0.74023, saving model to moblenet_face_detection.h5 1/134 [..............................] - ETA: 33s - loss: 0.7402 - dice_coefficient: 0.6051 Epoch 5: loss did not improve from 0.74023 2/134 [..............................] - ETA: 14s - loss: 0.8773 - dice_coefficient: 0.5283 Epoch 5: loss did not improve from 0.74023 3/134 [..............................] - ETA: 14s - loss: 0.8678 - dice_coefficient: 0.5468 Epoch 5: loss did not improve from 0.74023 4/134 [..............................] - ETA: 14s - loss: 0.8842 - dice_coefficient: 0.5454 Epoch 5: loss did not improve from 0.74023 5/134 [>.............................] - ETA: 14s - loss: 0.8908 - dice_coefficient: 0.5358 Epoch 5: loss did not improve from 0.74023 6/134 [>.............................] - ETA: 13s - loss: 0.8977 - dice_coefficient: 0.5414 Epoch 5: loss did not improve from 0.74023 7/134 [>.............................] - ETA: 13s - loss: 0.9043 - dice_coefficient: 0.5382 Epoch 5: loss did not improve from 0.74023 8/134 [>.............................] - ETA: 13s - loss: 0.9420 - dice_coefficient: 0.5124 Epoch 5: loss did not improve from 0.74023 9/134 [=>............................] - ETA: 13s - loss: 0.8991 - dice_coefficient: 0.5313 Epoch 5: loss did not improve from 0.74023 10/134 [=>............................] - ETA: 13s - loss: 0.8829 - dice_coefficient: 0.5304 Epoch 5: loss did not improve from 0.74023 11/134 [=>............................] - ETA: 13s - loss: 0.8864 - dice_coefficient: 0.5285 Epoch 5: loss did not improve from 0.74023 12/134 [=>............................] - ETA: 13s - loss: 0.8791 - dice_coefficient: 0.5296 Epoch 5: loss did not improve from 0.74023 13/134 [=>............................] - ETA: 12s - loss: 0.8683 - dice_coefficient: 0.5342 Epoch 5: loss did not improve from 0.74023 14/134 [==>...........................] - ETA: 12s - loss: 0.8610 - dice_coefficient: 0.5366 Epoch 5: loss did not improve from 0.74023 15/134 [==>...........................] - ETA: 12s - loss: 0.8774 - dice_coefficient: 0.5290 Epoch 5: loss did not improve from 0.74023 16/134 [==>...........................] - ETA: 12s - loss: 0.9272 - dice_coefficient: 0.5118 Epoch 5: loss did not improve from 0.74023 17/134 [==>...........................] - ETA: 12s - loss: 0.9128 - dice_coefficient: 0.5198 Epoch 5: loss did not improve from 0.74023 18/134 [===>..........................] - ETA: 12s - loss: 0.9192 - dice_coefficient: 0.5196 Epoch 5: loss did not improve from 0.74023 19/134 [===>..........................] - ETA: 12s - loss: 0.9399 - dice_coefficient: 0.5114 Epoch 5: loss did not improve from 0.74023 20/134 [===>..........................] - ETA: 12s - loss: 0.9362 - dice_coefficient: 0.5140 Epoch 5: loss did not improve from 0.74023 21/134 [===>..........................] - ETA: 12s - loss: 0.9450 - dice_coefficient: 0.5105 Epoch 5: loss did not improve from 0.74023 22/134 [===>..........................] - ETA: 12s - loss: 0.9505 - dice_coefficient: 0.5121 Epoch 5: loss did not improve from 0.74023 23/134 [====>.........................] - ETA: 11s - loss: 0.9446 - dice_coefficient: 0.5117 Epoch 5: loss did not improve from 0.74023 24/134 [====>.........................] - ETA: 11s - loss: 0.9597 - dice_coefficient: 0.5047 Epoch 5: loss did not improve from 0.74023 25/134 [====>.........................] - ETA: 11s - loss: 0.9501 - dice_coefficient: 0.5095 Epoch 5: loss did not improve from 0.74023 26/134 [====>.........................] - ETA: 11s - loss: 0.9475 - dice_coefficient: 0.5092 Epoch 5: loss did not improve from 0.74023 27/134 [=====>........................] - ETA: 11s - loss: 0.9561 - dice_coefficient: 0.5056 Epoch 5: loss did not improve from 0.74023 28/134 [=====>........................] - ETA: 11s - loss: 0.9631 - dice_coefficient: 0.5023 Epoch 5: loss did not improve from 0.74023 29/134 [=====>........................] - ETA: 11s - loss: 0.9544 - dice_coefficient: 0.5062 Epoch 5: loss did not improve from 0.74023 30/134 [=====>........................] - ETA: 11s - loss: 0.9484 - dice_coefficient: 0.5080 Epoch 5: loss did not improve from 0.74023 31/134 [=====>........................] - ETA: 11s - loss: 0.9529 - dice_coefficient: 0.5055 Epoch 5: loss did not improve from 0.74023 32/134 [======>.......................] - ETA: 10s - loss: 0.9584 - dice_coefficient: 0.5039 Epoch 5: loss did not improve from 0.74023 33/134 [======>.......................] - ETA: 10s - loss: 0.9546 - dice_coefficient: 0.5066 Epoch 5: loss did not improve from 0.74023 34/134 [======>.......................] - ETA: 10s - loss: 0.9542 - dice_coefficient: 0.5071 Epoch 5: loss did not improve from 0.74023 35/134 [======>.......................] - ETA: 10s - loss: 0.9507 - dice_coefficient: 0.5100 Epoch 5: loss did not improve from 0.74023 36/134 [=======>......................] - ETA: 10s - loss: 0.9451 - dice_coefficient: 0.5109 Epoch 5: loss did not improve from 0.74023 37/134 [=======>......................] - ETA: 10s - loss: 0.9478 - dice_coefficient: 0.5093 Epoch 5: loss did not improve from 0.74023 38/134 [=======>......................] - ETA: 10s - loss: 0.9458 - dice_coefficient: 0.5086 Epoch 5: loss did not improve from 0.74023 39/134 [=======>......................] - ETA: 10s - loss: 0.9547 - dice_coefficient: 0.5046 Epoch 5: loss did not improve from 0.74023 40/134 [=======>......................] - ETA: 10s - loss: 0.9547 - dice_coefficient: 0.5056 Epoch 5: loss did not improve from 0.74023 41/134 [========>.....................] - ETA: 10s - loss: 0.9545 - dice_coefficient: 0.5053 Epoch 5: loss did not improve from 0.74023 42/134 [========>.....................] - ETA: 9s - loss: 0.9576 - dice_coefficient: 0.5041 Epoch 5: loss did not improve from 0.74023 43/134 [========>.....................] - ETA: 9s - loss: 0.9560 - dice_coefficient: 0.5044 Epoch 5: loss did not improve from 0.74023 44/134 [========>.....................] - ETA: 9s - loss: 0.9514 - dice_coefficient: 0.5071 Epoch 5: loss did not improve from 0.74023 45/134 [=========>....................] - ETA: 9s - loss: 0.9544 - dice_coefficient: 0.5043 Epoch 5: loss did not improve from 0.74023 46/134 [=========>....................] - ETA: 9s - loss: 0.9563 - dice_coefficient: 0.5027 Epoch 5: loss did not improve from 0.74023 47/134 [=========>....................] - ETA: 9s - loss: 0.9551 - dice_coefficient: 0.5021 Epoch 5: loss did not improve from 0.74023 48/134 [=========>....................] - ETA: 9s - loss: 0.9595 - dice_coefficient: 0.5017 Epoch 5: loss did not improve from 0.74023 49/134 [=========>....................] - ETA: 9s - loss: 0.9561 - dice_coefficient: 0.5037 Epoch 5: loss did not improve from 0.74023 50/134 [==========>...................] - ETA: 9s - loss: 0.9537 - dice_coefficient: 0.5040 Epoch 5: loss did not improve from 0.74023 51/134 [==========>...................] - ETA: 8s - loss: 0.9518 - dice_coefficient: 0.5042 Epoch 5: loss did not improve from 0.74023 52/134 [==========>...................] - ETA: 8s - loss: 0.9546 - dice_coefficient: 0.5021 Epoch 5: loss did not improve from 0.74023 53/134 [==========>...................] - ETA: 8s - loss: 0.9488 - dice_coefficient: 0.5043 Epoch 5: loss did not improve from 0.74023 54/134 [===========>..................] - ETA: 8s - loss: 0.9472 - dice_coefficient: 0.5047 Epoch 5: loss did not improve from 0.74023 55/134 [===========>..................] - ETA: 8s - loss: 0.9423 - dice_coefficient: 0.5063 Epoch 5: loss did not improve from 0.74023 56/134 [===========>..................] - ETA: 8s - loss: 0.9381 - dice_coefficient: 0.5086 Epoch 5: loss did not improve from 0.74023 57/134 [===========>..................] - ETA: 8s - loss: 0.9401 - dice_coefficient: 0.5068 Epoch 5: loss did not improve from 0.74023 58/134 [===========>..................] - ETA: 8s - loss: 0.9417 - dice_coefficient: 0.5074 Epoch 5: loss did not improve from 0.74023 59/134 [============>.................] - ETA: 8s - loss: 0.9443 - dice_coefficient: 0.5069 Epoch 5: loss did not improve from 0.74023 60/134 [============>.................] - ETA: 7s - loss: 0.9409 - dice_coefficient: 0.5082 Epoch 5: loss did not improve from 0.74023 61/134 [============>.................] - ETA: 7s - loss: 0.9427 - dice_coefficient: 0.5075 Epoch 5: loss did not improve from 0.74023 62/134 [============>.................] - ETA: 7s - loss: 0.9411 - dice_coefficient: 0.5082 Epoch 5: loss did not improve from 0.74023 63/134 [=============>................] - ETA: 7s - loss: 0.9509 - dice_coefficient: 0.5040 Epoch 5: loss did not improve from 0.74023 64/134 [=============>................] - ETA: 7s - loss: 0.9466 - dice_coefficient: 0.5060 Epoch 5: loss did not improve from 0.74023 65/134 [=============>................] - ETA: 7s - loss: 0.9423 - dice_coefficient: 0.5083 Epoch 5: loss did not improve from 0.74023 66/134 [=============>................] - ETA: 7s - loss: 0.9393 - dice_coefficient: 0.5092 Epoch 5: loss did not improve from 0.74023 67/134 [==============>...............] - ETA: 7s - loss: 0.9386 - dice_coefficient: 0.5090 Epoch 5: loss did not improve from 0.74023 68/134 [==============>...............] - ETA: 7s - loss: 0.9385 - dice_coefficient: 0.5088 Epoch 5: loss did not improve from 0.74023 69/134 [==============>...............] - ETA: 7s - loss: 0.9388 - dice_coefficient: 0.5081 Epoch 5: loss did not improve from 0.74023 70/134 [==============>...............] - ETA: 6s - loss: 0.9379 - dice_coefficient: 0.5078 Epoch 5: loss did not improve from 0.74023 71/134 [==============>...............] - ETA: 6s - loss: 0.9429 - dice_coefficient: 0.5053 Epoch 5: loss did not improve from 0.74023 72/134 [===============>..............] - ETA: 6s - loss: 0.9424 - dice_coefficient: 0.5051 Epoch 5: loss did not improve from 0.74023 73/134 [===============>..............] - ETA: 6s - loss: 0.9470 - dice_coefficient: 0.5025 Epoch 5: loss did not improve from 0.74023 74/134 [===============>..............] - ETA: 6s - loss: 0.9469 - dice_coefficient: 0.5029 Epoch 5: loss did not improve from 0.74023 75/134 [===============>..............] - ETA: 6s - loss: 0.9435 - dice_coefficient: 0.5045 Epoch 5: loss did not improve from 0.74023 76/134 [================>.............] - ETA: 6s - loss: 0.9466 - dice_coefficient: 0.5032 Epoch 5: loss did not improve from 0.74023 77/134 [================>.............] - ETA: 6s - loss: 0.9421 - dice_coefficient: 0.5057 Epoch 5: loss did not improve from 0.74023 78/134 [================>.............] - ETA: 6s - loss: 0.9419 - dice_coefficient: 0.5053 Epoch 5: loss did not improve from 0.74023 79/134 [================>.............] - ETA: 5s - loss: 0.9386 - dice_coefficient: 0.5070 Epoch 5: loss did not improve from 0.74023 80/134 [================>.............] - ETA: 5s - loss: 0.9384 - dice_coefficient: 0.5079 Epoch 5: loss did not improve from 0.74023 81/134 [=================>............] - ETA: 5s - loss: 0.9351 - dice_coefficient: 0.5086 Epoch 5: loss did not improve from 0.74023 82/134 [=================>............] - ETA: 5s - loss: 0.9338 - dice_coefficient: 0.5097 Epoch 5: loss did not improve from 0.74023 83/134 [=================>............] - ETA: 5s - loss: 0.9352 - dice_coefficient: 0.5103 Epoch 5: loss did not improve from 0.74023 84/134 [=================>............] - ETA: 5s - loss: 0.9410 - dice_coefficient: 0.5104 Epoch 5: loss did not improve from 0.74023 85/134 [==================>...........] - ETA: 5s - loss: 0.9436 - dice_coefficient: 0.5087 Epoch 5: loss did not improve from 0.74023 86/134 [==================>...........] - ETA: 5s - loss: 0.9434 - dice_coefficient: 0.5089 Epoch 5: loss did not improve from 0.74023 87/134 [==================>...........] - ETA: 5s - loss: 0.9440 - dice_coefficient: 0.5089 Epoch 5: loss did not improve from 0.74023 88/134 [==================>...........] - ETA: 4s - loss: 0.9449 - dice_coefficient: 0.5093 Epoch 5: loss did not improve from 0.74023 89/134 [==================>...........] - ETA: 4s - loss: 0.9422 - dice_coefficient: 0.5101 Epoch 5: loss did not improve from 0.74023 90/134 [===================>..........] - ETA: 4s - loss: 0.9455 - dice_coefficient: 0.5087 Epoch 5: loss did not improve from 0.74023 91/134 [===================>..........] - ETA: 4s - loss: 0.9458 - dice_coefficient: 0.5081 Epoch 5: loss did not improve from 0.74023 92/134 [===================>..........] - ETA: 4s - loss: 0.9455 - dice_coefficient: 0.5082 Epoch 5: loss did not improve from 0.74023 93/134 [===================>..........] - ETA: 4s - loss: 0.9453 - dice_coefficient: 0.5079 Epoch 5: loss did not improve from 0.74023 94/134 [====================>.........] - ETA: 4s - loss: 0.9469 - dice_coefficient: 0.5072 Epoch 5: loss did not improve from 0.74023 95/134 [====================>.........] - ETA: 4s - loss: 0.9508 - dice_coefficient: 0.5054 Epoch 5: loss did not improve from 0.74023 96/134 [====================>.........] - ETA: 4s - loss: 0.9521 - dice_coefficient: 0.5047 Epoch 5: loss did not improve from 0.74023 97/134 [====================>.........] - ETA: 3s - loss: 0.9525 - dice_coefficient: 0.5054 Epoch 5: loss did not improve from 0.74023 98/134 [====================>.........] - ETA: 3s - loss: 0.9502 - dice_coefficient: 0.5075 Epoch 5: loss did not improve from 0.74023 99/134 [=====================>........] - ETA: 3s - loss: 0.9525 - dice_coefficient: 0.5065 Epoch 5: loss did not improve from 0.74023 100/134 [=====================>........] - ETA: 3s - loss: 0.9497 - dice_coefficient: 0.5079 Epoch 5: loss did not improve from 0.74023 101/134 [=====================>........] - ETA: 3s - loss: 0.9479 - dice_coefficient: 0.5093 Epoch 5: loss did not improve from 0.74023 102/134 [=====================>........] - ETA: 3s - loss: 0.9468 - dice_coefficient: 0.5091 Epoch 5: loss did not improve from 0.74023 103/134 [======================>.......] - ETA: 3s - loss: 0.9459 - dice_coefficient: 0.5088 Epoch 5: loss did not improve from 0.74023 104/134 [======================>.......] - ETA: 3s - loss: 0.9451 - dice_coefficient: 0.5087 Epoch 5: loss did not improve from 0.74023 105/134 [======================>.......] - ETA: 3s - loss: 0.9445 - dice_coefficient: 0.5086 Epoch 5: loss did not improve from 0.74023 106/134 [======================>.......] - ETA: 3s - loss: 0.9445 - dice_coefficient: 0.5085 Epoch 5: loss did not improve from 0.74023 107/134 [======================>.......] - ETA: 2s - loss: 0.9431 - dice_coefficient: 0.5088 Epoch 5: loss did not improve from 0.74023 108/134 [=======================>......] - ETA: 2s - loss: 0.9403 - dice_coefficient: 0.5102 Epoch 5: loss did not improve from 0.74023 109/134 [=======================>......] - ETA: 2s - loss: 0.9387 - dice_coefficient: 0.5115 Epoch 5: loss did not improve from 0.74023 110/134 [=======================>......] - ETA: 2s - loss: 0.9355 - dice_coefficient: 0.5132 Epoch 5: loss did not improve from 0.74023 111/134 [=======================>......] - ETA: 2s - loss: 0.9337 - dice_coefficient: 0.5140 Epoch 5: loss did not improve from 0.74023 112/134 [========================>.....] - ETA: 2s - loss: 0.9343 - dice_coefficient: 0.5135 Epoch 5: loss did not improve from 0.74023 113/134 [========================>.....] - ETA: 2s - loss: 0.9327 - dice_coefficient: 0.5145 Epoch 5: loss did not improve from 0.74023 114/134 [========================>.....] - ETA: 2s - loss: 0.9302 - dice_coefficient: 0.5155 Epoch 5: loss did not improve from 0.74023 115/134 [========================>.....] - ETA: 2s - loss: 0.9285 - dice_coefficient: 0.5166 Epoch 5: loss did not improve from 0.74023 116/134 [========================>.....] - ETA: 1s - loss: 0.9283 - dice_coefficient: 0.5162 Epoch 5: loss did not improve from 0.74023 117/134 [=========================>....] - ETA: 1s - loss: 0.9279 - dice_coefficient: 0.5170 Epoch 5: loss did not improve from 0.74023 118/134 [=========================>....] - ETA: 1s - loss: 0.9269 - dice_coefficient: 0.5168 Epoch 5: loss did not improve from 0.74023 119/134 [=========================>....] - ETA: 1s - loss: 0.9272 - dice_coefficient: 0.5165 Epoch 5: loss did not improve from 0.74023 120/134 [=========================>....] - ETA: 1s - loss: 0.9253 - dice_coefficient: 0.5179 Epoch 5: loss did not improve from 0.74023 121/134 [==========================>...] - ETA: 1s - loss: 0.9236 - dice_coefficient: 0.5183 Epoch 5: loss did not improve from 0.74023 122/134 [==========================>...] - ETA: 1s - loss: 0.9256 - dice_coefficient: 0.5177 Epoch 5: loss did not improve from 0.74023 123/134 [==========================>...] - ETA: 1s - loss: 0.9248 - dice_coefficient: 0.5188 Epoch 5: loss did not improve from 0.74023 124/134 [==========================>...] - ETA: 1s - loss: 0.9229 - dice_coefficient: 0.5195 Epoch 5: loss did not improve from 0.74023 125/134 [==========================>...] - ETA: 0s - loss: 0.9249 - dice_coefficient: 0.5193 Epoch 5: loss did not improve from 0.74023 126/134 [===========================>..] - ETA: 0s - loss: 0.9246 - dice_coefficient: 0.5195 Epoch 5: loss did not improve from 0.74023 127/134 [===========================>..] - ETA: 0s - loss: 0.9226 - dice_coefficient: 0.5207 Epoch 5: loss did not improve from 0.74023 128/134 [===========================>..] - ETA: 0s - loss: 0.9209 - dice_coefficient: 0.5213 Epoch 5: loss did not improve from 0.74023 129/134 [===========================>..] - ETA: 0s - loss: 0.9211 - dice_coefficient: 0.5207 Epoch 5: loss did not improve from 0.74023 130/134 [============================>.] - ETA: 0s - loss: 0.9205 - dice_coefficient: 0.5208 Epoch 5: loss did not improve from 0.74023 131/134 [============================>.] - ETA: 0s - loss: 0.9193 - dice_coefficient: 0.5219 Epoch 5: loss did not improve from 0.74023 132/134 [============================>.] - ETA: 0s - loss: 0.9181 - dice_coefficient: 0.5225 Epoch 5: loss did not improve from 0.74023 133/134 [============================>.] - ETA: 0s - loss: 0.9180 - dice_coefficient: 0.5223 Epoch 5: loss did not improve from 0.74023 134/134 [==============================] - 15s 108ms/step - loss: 0.9183 - dice_coefficient: 0.5224 - lr: 1.0000e-04
<keras.callbacks.History at 0x7ff86f765dc0>
2.D.
model.evaluate(X_test, y_test)
1/1 [==============================] - 1s 1s/step - loss: 0.7505 - dice_coefficient: 0.6019
[0.7504779100418091, 0.6019384860992432]
we were able to get a Dice Coff which is above 50%
3.
test_image = X_test[3]
plt.imshow(test_image)
test_image = cv2.resize(test_image, (IMAGE_WIDTH, IMAGE_HEIGHT))
feat_scaled = np.array(test_image, dtype=np.float32)
y_pred = model.predict(np.array([feat_scaled]))
WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
1/1 [==============================] - 0s 444ms/step
pred_mask = cv2.resize((1.0*(y_pred[0] > 0.5)), (IMAGE_WIDTH, IMAGE_HEIGHT))
plt.imshow(feat_scaled)
plt.show()
plt.imshow(pred_mask)
plt.show()
WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
1.
zipfile_path = os.path.join(folderPath, "training_images")
zipfile_path
'/content/drive/MyDrive/Colab4GPU/Project2/training_images'
from zipfile import ZipFile
with ZipFile(zipfile_path + '.zip', 'r') as z:
print("Zip already extracted") if os.path.exists(zipfile_path) else z.extractall(path=folderPath)
import glob
import re
import skimage
training_images_paths = glob.glob(zipfile_path + "/*")
training_images_paths
['/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00947.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01052.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01042.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00964.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01053.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00971.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01057.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01044.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01041.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01067.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00969.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00956.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00975.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01021.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00945.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01068.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01062.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01016.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01065.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01027.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00961.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01014.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01010.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00952.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00951.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00981.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01050.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00962.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00995.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01056.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00977.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00985.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01005.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01031.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01008.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01073.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00978.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01077.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00984.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01032.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00943.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01015.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00970.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01037.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01080.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00954.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00967.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01047.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01039.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01006.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01075.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01017.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01079.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01049.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01040.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00958.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00944.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01007(1).jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01061.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01011.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01000.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00963.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01036.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01069.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00972.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00996.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00959.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01055.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01034.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00973.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00955.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00946.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01029.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01063.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01060.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00976.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00974.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00993.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01038.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00988.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00950.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00997.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01076.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00990.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00982.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01022.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01007.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01013.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01030.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01054.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00968.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01035.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01051.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01043.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00999.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01071.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01026.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00979.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00987.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01048.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00991.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01023.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01020.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00983.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00948.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00953.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00960.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00942.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01059.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00980.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01072.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00989.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01058.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01018.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01045.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00986.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_01066.jpg', 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'/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00152.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00156.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00099.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00165.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00138.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00051.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00087.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00160.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00058.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00155.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00042.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00089.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00026.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00030.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00081.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00068.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00053.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00170.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00300.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00108.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00168.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00154.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00102.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00158.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00084.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00161.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00140.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00039.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00023.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00145.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00033.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00056.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00034.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00111.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00150.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00094.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00132.jpg', '/content/drive/MyDrive/Colab4GPU/Project2/training_images/real_00057.jpg', ...]
2.
haarcascade_frontalface_default_path = folderPath + "/haarcascade_frontalface_default.xml"
haarcascade_frontalface_default_path
'/content/drive/MyDrive/Colab4GPU/Project2/haarcascade_frontalface_default.xml'
cascade_model = cv2.CascadeClassifier(haarcascade_frontalface_default_path)
def extract_face(image):
# Gets the face from the image passed using Haar-Cascade
global cascade_model
# Getting co-ordinates of the face
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
faces = cascade_model.detectMultiScale(
gray,
scaleFactor=1.4,
minNeighbors=5,
minSize=(32, 32)
)
# Extracts the face from the image and returns it
if len(faces)>0:
x,y,w,h = faces[-1]
return x,y,w,h, len(faces)
return 0,0,0,0,0
df = pd.DataFrame(columns=('x','y','w','h','Total_Faces','Image_Name','pred_mask'))
for image_path in training_images_paths[:20]:
image_name = re.split(r'[/\\]', image_path)[-1]
image = cv2.imread(os.path.join(image_path))
x,y,w,h, count = extract_face(image)
df.loc[len(df)] = [x,y,w,h,count,image_name,pred_mask]
3.
df
| x | y | w | h | Total_Faces | Image_Name | pred_mask | |
|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 0 | 0 | 0 | real_00947.jpg | [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... |
| 1 | 0 | 0 | 0 | 0 | 0 | real_01052.jpg | [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... |
| 2 | 49 | 159 | 354 | 354 | 2 | real_01042.jpg | [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... |
| 3 | 26 | 90 | 496 | 496 | 1 | real_00964.jpg | [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... |
| 4 | 59 | 45 | 496 | 496 | 1 | real_01053.jpg | [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... |
| 5 | 0 | 0 | 0 | 0 | 0 | real_00971.jpg | [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... |
| 6 | 139 | 186 | 354 | 354 | 1 | real_01057.jpg | [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... |
| 7 | 183 | 176 | 354 | 354 | 1 | real_01044.jpg | [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... |
| 8 | 173 | 142 | 354 | 354 | 1 | real_01041.jpg | [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... |
| 9 | 0 | 0 | 0 | 0 | 0 | real_01067.jpg | [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... |
| 10 | 0 | 0 | 0 | 0 | 0 | real_00969.jpg | [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... |
| 11 | 164 | 171 | 354 | 354 | 1 | real_00956.jpg | [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... |
| 12 | 148 | 131 | 354 | 354 | 2 | real_00975.jpg | [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... |
| 13 | 64 | 79 | 496 | 496 | 1 | real_01021.jpg | [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... |
| 14 | 39 | 72 | 496 | 496 | 1 | real_00945.jpg | [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... |
| 15 | 111 | 175 | 354 | 354 | 1 | real_01068.jpg | [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... |
| 16 | 36 | 162 | 354 | 354 | 1 | real_01062.jpg | [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... |
| 17 | 115 | 154 | 354 | 354 | 2 | real_01016.jpg | [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... |
| 18 | 0 | 0 | 0 | 0 | 0 | real_01065.jpg | [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... |
| 19 | 81 | 69 | 496 | 496 | 1 | real_01027.jpg | [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... |
4.
df.to_csv(folderPath + '/ExtractedFaces.csv')
1.
zipfile_path = os.path.join(folderPath, "PINS")
zipfile_path
'/content/drive/MyDrive/Colab4GPU/Project2/PINS'
from zipfile import ZipFile
with ZipFile(zipfile_path + '.zip', 'r') as z:
print("Zip already extracted") if os.path.exists(zipfile_path) else z.extractall(path=folderPath)
class IdentityMetadata():
def __init__(self, name, base, foldername, file):
# identity name
self.name = name
# dataset base directory
self.base = base
# folder name
self.foldername = foldername
# image file name
self.file = file
def __repr__(self):
return self.image_path()
def image_path(self):
return os.path.join(self.base, self.foldername, self.file)
def load_metadata(path):
metadata = []
for i in os.listdir(path):
for f in os.listdir(os.path.join(path, i)):
# Check file extension. Allow only jpg/jpeg' files.
ext = os.path.splitext(f)[1]
if ext == '.jpg' or ext == '.jpeg':
name = i.split('_')[-1]
metadata.append(IdentityMetadata(name, path, i, f))
return np.array(metadata)
metadata = load_metadata(zipfile_path)
metadata.shape
(10770,)
print("NAME : ", metadata[1].name)
print("base folder :", metadata[1].base)
print("image folder :", metadata[1].foldername)
print("image file name :", metadata[1].file)
NAME : mark zuckerberg base folder : /content/drive/MyDrive/Colab4GPU/Project2/PINS image folder : pins_mark zuckerberg image file name : mark zuckerberg50.jpg
4.
def load_image(path):
img = cv2.imread(path, 1)
return img[...,::-1]
n = 15
img_path = metadata[n].image_path()
img = load_image(img_path)
fig = plt.figure(figsize = (15, 7.2))
ax = fig.add_subplot(1, 1, 1)
title = metadata[n].name
ax.set_title(title, fontsize = 20)
_ = plt.imshow(img)
def vgg_face():
model = Sequential()
model.add(ZeroPadding2D((1,1),input_shape=(224,224, 3)))
model.add(Convolution2D(64, (3, 3), activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(128, (3, 3), activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(128, (3, 3), activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(256, (3, 3), activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(256, (3, 3), activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(256, (3, 3), activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, (3, 3), activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, (3, 3), activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, (3, 3), activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, (3, 3), activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, (3, 3), activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, (3, 3), activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))
model.add(Convolution2D(4096, (7, 7), activation='relu'))
model.add(Dropout(0.5))
model.add(Convolution2D(4096, (1, 1), activation='relu'))
model.add(Dropout(0.5))
model.add(Convolution2D(2622, (1, 1)))
model.add(Flatten())
model.add(Activation('softmax'))
return model
model = vgg_face()
model.load_weights(folderPath +'/vgg_face_weights.h5')
print(model.summary())
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
zero_padding2d (ZeroPadding (None, 226, 226, 3) 0
2D)
conv2d_1 (Conv2D) (None, 224, 224, 64) 1792
zero_padding2d_1 (ZeroPaddi (None, 226, 226, 64) 0
ng2D)
conv2d_2 (Conv2D) (None, 224, 224, 64) 36928
max_pooling2d (MaxPooling2D (None, 112, 112, 64) 0
)
zero_padding2d_2 (ZeroPaddi (None, 114, 114, 64) 0
ng2D)
conv2d_3 (Conv2D) (None, 112, 112, 128) 73856
zero_padding2d_3 (ZeroPaddi (None, 114, 114, 128) 0
ng2D)
conv2d_4 (Conv2D) (None, 112, 112, 128) 147584
max_pooling2d_1 (MaxPooling (None, 56, 56, 128) 0
2D)
zero_padding2d_4 (ZeroPaddi (None, 58, 58, 128) 0
ng2D)
conv2d_5 (Conv2D) (None, 56, 56, 256) 295168
zero_padding2d_5 (ZeroPaddi (None, 58, 58, 256) 0
ng2D)
conv2d_6 (Conv2D) (None, 56, 56, 256) 590080
zero_padding2d_6 (ZeroPaddi (None, 58, 58, 256) 0
ng2D)
conv2d_7 (Conv2D) (None, 56, 56, 256) 590080
max_pooling2d_2 (MaxPooling (None, 28, 28, 256) 0
2D)
zero_padding2d_7 (ZeroPaddi (None, 30, 30, 256) 0
ng2D)
conv2d_8 (Conv2D) (None, 28, 28, 512) 1180160
zero_padding2d_8 (ZeroPaddi (None, 30, 30, 512) 0
ng2D)
conv2d_9 (Conv2D) (None, 28, 28, 512) 2359808
zero_padding2d_9 (ZeroPaddi (None, 30, 30, 512) 0
ng2D)
conv2d_10 (Conv2D) (None, 28, 28, 512) 2359808
max_pooling2d_3 (MaxPooling (None, 14, 14, 512) 0
2D)
zero_padding2d_10 (ZeroPadd (None, 16, 16, 512) 0
ing2D)
conv2d_11 (Conv2D) (None, 14, 14, 512) 2359808
zero_padding2d_11 (ZeroPadd (None, 16, 16, 512) 0
ing2D)
conv2d_12 (Conv2D) (None, 14, 14, 512) 2359808
zero_padding2d_12 (ZeroPadd (None, 16, 16, 512) 0
ing2D)
conv2d_13 (Conv2D) (None, 14, 14, 512) 2359808
max_pooling2d_4 (MaxPooling (None, 7, 7, 512) 0
2D)
conv2d_14 (Conv2D) (None, 1, 1, 4096) 102764544
dropout (Dropout) (None, 1, 1, 4096) 0
conv2d_15 (Conv2D) (None, 1, 1, 4096) 16781312
dropout_1 (Dropout) (None, 1, 1, 4096) 0
conv2d_16 (Conv2D) (None, 1, 1, 2622) 10742334
flatten (Flatten) (None, 2622) 0
activation (Activation) (None, 2622) 0
=================================================================
Total params: 145,002,878
Trainable params: 145,002,878
Non-trainable params: 0
_________________________________________________________________
None
from tensorflow.keras.models import Model
vgg_face_descriptor = Model(inputs=model.layers[0].input, outputs=model.layers[-2].output)
img_path = metadata[0].image_path()
img = load_image(img_path)
# Normalising pixel values from [0-255] to [0-1]: scale RGB values to interval [0,1]
img = (img / 255.).astype(np.float32)
img = cv2.resize(img, dsize = (224,224))
print(img.shape)
# Obtain embedding vector for an image
# Get the embedding vector for the above image using vgg_face_descriptor model and print the shape
embedding_vector = vgg_face_descriptor.predict(np.expand_dims(img, axis=0))[0]
print(embedding_vector.shape)
(224, 224, 3) 1/1 [==============================] - 1s 1s/step (2622,)
from tqdm import tqdm
embeddings = []
embeddings = np.zeros((metadata.shape[0], 2622))
for i, meta in tqdm(enumerate(metadata)):
try:
image = load_image(str(meta))
image = (image/255.).astype(np.float32)
image = cv2.resize(image, (224, 224))
embeddings[i] = vgg_face_descriptor.predict(np.expand_dims(image, axis = 0))[0]
except:
embeddings[i] = np.zeros(2622)
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5.
def distance_btw_photo(emb1, emb2):
return np.sum(np.square(emb1 - emb2))
def photo_pair(idx1, idx2):
plt.figure(figsize=(10,3))
plt.suptitle(f'Distance = {distance_btw_photo(embeddings[idx1], embeddings[idx2]):.2f}')
plt.subplot(121)
plt.imshow(load_image(metadata[idx1].image_path()))
plt.subplot(122)
plt.imshow(load_image(metadata[idx2].image_path()));
photo_pair(2, 3)
photo_pair(2, 180)
photo_pair(30, 31)
photo_pair(30, 100)
photo_pair(70, 72)
photo_pair(70, 115)
6.
train_idx = np.arange(metadata.shape[0]) % 9 != 0 #every 9th example goes in test data and rest go in train data
test_idx = np.arange(metadata.shape[0]) % 9 == 0
X_train = embeddings[train_idx]
X_test = embeddings[test_idx]
targets = np.array([m.name for m in metadata])
y_train = targets[train_idx]
y_test = targets[test_idx]
display(X_train.shape, X_test.shape, y_train.shape, y_test.shape)
(9573, 2622)
(1197, 2622)
(9573,)
(1197,)
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
y_train = le.fit_transform(y_train)
y_test = le.transform(y_test)
from sklearn.preprocessing import StandardScaler
SS= StandardScaler()
X_train_ss = SS.fit_transform(X_train)
X_test_ss = SS.transform(X_test)
print(X_train_ss)
[[ 1.01259599 1.50950177 0.29281411 ... 0.41421063 0.69123451 -1.17165333] [-0.30229644 3.12414742 1.94676532 ... 0.82522083 1.00856709 0.2849892 ] [ 0.38452147 1.31208742 -0.68565779 ... 0.964679 1.59038878 0.54344618] ... [-0.5887049 -0.18055319 -0.4695441 ... 0.28115156 1.40175565 0.37727055] [-1.19820354 -0.50629338 -0.70875397 ... 0.79512488 0.43267639 0.26863984] [-0.95795935 -0.41344596 0.44561798 ... 0.8014221 0.9145346 0.11207892]]
from sklearn.decomposition import PCA
n_components = 128
pca = PCA(n_components=n_components, whiten=True)
pca.fit(X_train_ss)
X_train_pca = pca.transform(X_train_ss)
X_test_pca = pca.transform(X_test_ss)
7.
from sklearn.svm import SVC
model_svc= SVC(kernel='linear')
model_svc.fit(X_train_pca, y_train)
print('Accuracy for svc train set: {0:.6f}'.format(model_svc.score(X_train_pca, y_train)))
Accuracy for svc train set: 0.999791
example_idx = 10
example_image = load_image(metadata[test_idx][example_idx].image_path())
example_prediction = model_svc.predict([X_test_pca[example_idx]])
example_identity = le.inverse_transform(example_prediction)[0]
plt.imshow(example_image)
plt.title(f'Identified as {example_identity}');
